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ISBN: 0-8247-8981-4 This book is printed on acid-free paper. Headquarters Marcel, Dekker, Inc. 270 Madison Avenue, New York, NY 10016 tel: 212-696-9000; fax: 212-685-4540 Eastern Hemisphere Distribution Marcel Dekker AG Hutgasse 4, Postfach 812, CH-4001 Basel, Switzerland tel: 41-61-261-8482; fax: 41-61-261-8996 World Wide Web http://www.dekker.com The publisher offers discounts on this book when ordered in bulk quantities. For more information, write to Special Sales/Professional Marketing at the headquarters address above. Copyright # 2002 by Marcel Dekker, Inc. All Rights Reserved. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage and retrieval system, without permission in writing from the publisher. Current printing (last digit): 10 9 8 7 6 5 4 3 2 1 PRINTED IN THE UNITED STATES OF AMERICA
To my wife, Mary, and our son, Nikos
Series Introduction
Many textbooks have been written on control engineering, describing new techniques for controlling systems, or new and better ways of mathematically formulating existing methods to solve the ever-increasing complex problems faced by practicing engineers. However, few of these books fully address the applications aspects of control engineering. It is the intention of this new series to redress this situation. The series will stress applications issues, and not just the mathematics of control engineering. It will provide texts that present not only both new and well-established techniques, but also detailed examples of the application of these methods to the solution of real-world problems. The authors will be drawn from both the academic world and the relevant applications sectors. There are already many exciting examples of the application of control techniques in the established fields of electrical, mechanical (including aerospace), and chemical engineering. We have only to look around in today’s highly automated society to see the use of advanced robotics techniques in the manufacturing industries; the use of automated control and navigation systems in air and surface transport systems; the increasing use of intelligent control systems in the many artifacts available to the domestic consumer market; and the reliable supply of water, gas, and electrical power to the domestic consumer and to industry. However, there are currently many challenging problems that could benefit from wider exposure to the applicability of control methodologies, and the systematic systems-oriented basis inherent in the application of control techniques. This series presents books that draw on expertise from both the academic world and the applications domains, and will be useful not only as academically recommended course texts but also as handbooks for practitioners in many applications domains. Modern Control Engineering is another outstanding entry to Dekker’s Control Engineering series. Neil Munro v
Preface
Automatic control is one of today’s most significant areas of science and technology. This can be attributed to the fact that automation is linked to the development of almost every form of technology. By its very nature, automatic control is a multidisciplinary subject; it constitutes a core course in many engineering departments, such as electrical, electronic, mechanical, chemical, and aeronautical. Automatic control requires both a rather strong mathematical foundation, and implementation skills to work with controllers in practice. The goal of this book is to present control engineering methods using only the essential mathematical tools and to stress the application procedures and skills by giving insight into physical system behavior and characteristics. Overall, the approach used herein is to help the student understand and assimilate the basic concepts in control system modeling, analysis, and design. Automatic control has developed rapidly over the last 60 years. An impressive boost to this development was provided by the technologies that grew out of space exploration and World War II. In the last 20 years, automatic control has undergone a significant and rapid development due mainly to digital computers. Indeed, recent developments in digital computers—especially their increasingly low cost—facilitate their use in controlling complex systems and processes. Automatic control is a vast technological area whose central aim is to develop control strategies that improve performance when they are applied to a system or a process. The results reported thus far on control design techniques are significant from both a theoretical and a practical perspective. From the theoretical perspective, these results are presented in great depth, covering a wide variety of modern control problems, such as optimal and stochastic control, adaptive and robust control, Kalman filtering, and system identification. From the practical point of view, these results have been successfully implemented in numerous practical systems and processes—for example, in controlling temperature, pressure, and fluid level; in electrical energy plants; in industrial plants producing paper, cement, steel, sugar, plastics, clothes, and food; in nuclear and chemical reactors; in ground, sea, and air vii
viii
Preface
transportation systems; and in robotics, space applications, farming, biotechnology, and medicine. I should note that classical control techniques—especially those using proportional-integral-derivative (PID) controllers, which have existed since 1942—predominate in the overall practice of control engineering today. Despite the impressive progress since the 1940s, practical applications of modern control techniques are limited. This is indeed a serious gap between theory and practice. To reduce this gap, techniques of modern control engineering should be designed with an eye toward applicability, so as to facilitate their use in practice. To this end, modern control techniques must be presented in a simple and user-friendly fashion to engineering students in introductory control courses, so that these techniques may find immediate and widespread application. In turn, control engineering could serve human needs better and provide the same breadth of technological application found in other, related areas, such as communications and computer science. This book has been written in this spirit. Modern Control Engineering is based on the introductory course on control systems that I teach to junior undergraduate students in the Department of Electrical and Computer Engineering at the National Technical University of Athens. It begins with a description and analysis of linear time-invariant systems. Next, classical (Bode and Nyquist diagrams, the root locus, compensating networks, and PID controllers) and modern (pole placement, state observers, and optimal control) controller design techniques are presented. Subsequent chapters cover more advanced techniques of modern control: digital control, system identification, adaptive control, robust control, and fuzzy control. This text is thus appropriate for undergraduate and first-year graduate courses in modern control engineering, and it should also prove useful for practicing engineers. The book has 16 chapters, which may be grouped into two parts: Classical Control (Chapters 1 through 9) and Modern Control (Chapters 10 through 16). (Please note that, throughout the book, bold lowercase letters indicate vectors and bold capital letters indicate matrices.)
CLASSICAL CONTROL Chapter 1 is an introduction to automatic control systems. Chapter 2 presents the Laplace transform and matrix theory, which is a necessary mathematical background for studying continuous-time systems. Chapter 3 describes and analyzes linear time-invariant systems by using the following mathematical models: differential equations, transfer functions, impulse response, and state-space equations; the topics of block diagrams and signal-flow graphs are also covered. Chapter 4 describes classical time-domain analysis, covering topics such as time response, model simplification, comparison of open- and closed-loop systems, model reduction, sensitivity analysis, steady-state errors, and disturbance rejection. Chapter 5 describes state-space analysis of linear systems and discusses the important concepts of controllability and observability, along with their relation to the transfer function. Chapter 6 discusses the important problem of stability. It covers the algebraic criteria of Ruth, Hurwitz, and continuous fraction, and provides an introduction to the stability of nonlinear and linear systems using the Lyapunov methods.
Preface
ix
Chapter 7 covers the popular root locus method. Chapter 8 describes the frequency response of linear time-invariant systems, introducing the three wellknown frequency domain methods: those of Nyquist, Bode, and Nichols. Chapter 9 is devoted to the classical design techniques, emphasizing controller design methods using controllers of the following types: PID, phase-lead, phase-lag, and phase lead-lag. The chapter also presents an introduction to classical optimal control. MODERN CONTROL Chapters 10 and 11 focus on modern controller design techniques carried out in state-space. Chapter 10 covers the design problems of pole assignment, input-output decoupling, model matching, and state observers. Closed-loop system design using state observers is also explained. Chapter 11 elucidates the problem of optimal control, as illustrated in the optimal regulator and servomechanism problems. Chapter 12 is an introduction to digital control that provides extensive coverage of basic problems in discrete-time system description, analysis, stability, controllability, observability, and classical control techniques. Chapter 13 explains discrete-time system identification and gives the basic algorithms for off-line and on-line parametric identification. Chapter 14 covers discrete-time system adaptive control. The following four adaptive schemes are presented: the gradient method (MIT rule), model reference, adaptive control, and self-tuning regulators. Chapter 15 is an introduction to robust control, focusing on topics such as model uncertainty, robust stability, robust performance, and Kharitonov s theorem. Chapter 16 is an introduction to fuzzy control, emphasizing the design of fuzzy controllers. The book concludes with three appendixes that provide useful background information. Appendix A presents the Laplace transform tables, Appendix B demonstrates the Z-transform technique necessary for analyzing and designing the discrete-time (or digital) control systems presented in Chapter 12, and Appendix C gives the Ztransform tables. ACKNOWLEDGMENTS I would like to thank very much my undergraduate students A. Dimeas and D. Kazizis for preparing the figures and tables, my graduate student A. Vernardos for proofreading the typewritten manuscript, and Dr. Iliana Gravalou for her help in checking most solved examples and in preparing the Solutions Manual. Many thanks to Dr. Argiris Soldatos for carefully reading several chapters. Special thanks are also due Dr. K. Arvanitis for his assistance in formulating the material of Chapter 15 and to Professors R. E. King and G. Bitsoris, my colleagues, for their numerous suggestions. P. N. Paraskevopoulos
Contents
Series Introdution Preface
v vii
Part I Classical Control 1.
Introduction to Automatic Control Systems
1
2.
Mathematical Background
27
3.
Mathematical Models of Systems
67
4.
Classical Time-Domain Analysis of Control Systems
147
5.
State-Space Analysis of Control Systems
193
6.
Stability
237
7.
The Root Locus Method
271
8.
Frequency Domain Analysis
305
9.
Classical Control Design Methods
367
Part II Modern Control 10.
State-Space Design Methods
435
11.
Optimal Control
479
12.
Digital Control
515
13.
System Identification
583 xi
xii
Contents
14.
Adaptive Control
603
15.
Robust Control
637
16.
Fuzzy Control
673
Appendix A: Laplace Transform Tables Appendix B: The Z-Transform Appendix C: Z-Transform Tables Index
701 707 723 729
1 Introduction to Automatic Control Systems
1.1
INTRODUCTION
An automatic control system is a combination of components that act together in such a way that the overall system behaves automatically in a prespecified desired manner. A close examination of the various machines and apparatus that are manufactured today leads to the conclusion that they are partially or entirely automated, e.g., the refrigerator, the water heater, the clothes washing machine, the elevator, the TV remote control, the worldwide telephone communication systems, and the Internet. Industries are also partially or entirely automated, e.g., the food, paper, cement, and car industries. Examples from other areas of control applications abound: electrical power plants, reactors (nuclear and chemical), transportation systems (cars, airplanes, ships, helicopters, submarines, etc.), robots (for assembly, welding, etc.), weapon systems (fire control systems, missiles, etc.), computers (printers, disk drives, magnetic tapes, etc.), farming (greenhouses, irrigation, etc.), and many others, such as control of position or velocity, temperature, voltage, pressure, fluid level, traffic, and office automation, computer-integrated manufacturing, and energy management for buildings. All these examples lead to the conclusion that automatic control is used in all facets of human technical activities and contributes to the advancement of modern technology. The distinct characteristic of automatic control is that it reduces, as much as possible, the human participation in all the aforementioned technical activities. This usually results in decreasing labor cost, which in turn allows the production of more goods and the construction of more works. Furthermore, automatic control reduces work hazards, while it contributes in reducing working hours, thus offering to working people a better quality of life (more free time to rest, develop hobbies, have fun, etc.). Automatic control is a subject which is met not only in technology but also in other areas such as biology, medicine, economics, management, and social sciences. In particular, with regard to biology, one can claim that plants and animals owe their 1
2
Chapter 1
very existence to control. To understand this point, consider for example the human body, where a tremendous number of processes take place automatically: hunger, thirst, digestion, respiration, body temperature, blood circulation, reproduction of cells, healing of wounds, etc. Also, think of the fact that we don’t even decide when to drink, when to eat, when to go to sleep, and when to go to the toilet. Clearly, no form of life could exist if it were not for the numerous control systems that govern all processes in every living organism. It is important to mention that modern technology has, in certain cases, succeeded in replacing body organs or mechanisms, as for example in replacing a human hand, cut off at the wrist, with an artificial hand that can move its fingers automatically, as if it were a natural hand. Although the use of this artificial hand is usually limited to simple tasks, such as opening a door, lifting an object, and eating, all these functions are a great relief to people who were unfortunate enough to lose a hand.
1.2
A BRIEF HISTORICAL REVIEW OF AUTOMATIC CONTROL SYSTEMS
Control systems have been in existence since ancient times. A well-known ancient automatic control system is the regulator of Heron of Alexandria (Figure 1.1). This control system was designed to open the doors of a temple automatically when a fire was lit at the altar located outside the temple and to close the doors when the fire was put out. In particular, the regulator operated in the following way: the fire, acting as the input to the system, heated the air underneath the altar and the warm (expanded)
Figure 1.1
The regulator of Heron of Alexandria.
Automatic Control Systems
3
air pushed the water from the water container (pot 1) to the bucket (pot 2). The position of the water container was fixed, while the bucket was hanging from ropes wrapped around a mechanism (the door spindles) with a counterweight W. When pot 2 was empty, this mechanism, under the pull of the counterweight W, held the doors closed. When pot 2 was filled with adequate amount of water from pot 1, it moved downwards, while the counterweight W moved upwards. As a result of the downward motion of pot 2, the door spindles turned and the doors opened. When the fire was put out, water from pot 2 returned to pot 1, and the counterweight W moved downwards forcing the gates to close. Apparently, this control system was used to impress believers, since it was not visible or known to the masses (it was hidden underground). Until about the middle of the 18th century, automatic control has no particular progress to show. The use of control started to advance in the second half of the 18th century, due to James Watt, who, in 1769, invented the first centrifugal speed regulator (Figure 1.2) which subsequently has been widely used in practice, most often for the control of locomotives. In particular, this regulator was used to control the speed of the steam engine. This is accomplished as follows: as the angular velocity of the steam engine increases, the centrifugal force pushes the masses m upwards and the steam valve closes. As the steam valve closes, the steam entering the engine from the boiler is reduced and the steam engine’s angular velocity decreases, and vice versa: as the angular velocity of the steam engine decreases, the masses m go down, the steam valve opens, the amount of steam entering the engine increases, resulting in an increase of the angular velocity. This way, one can regulate the speed of the engine. The period until about the middle of the 19th century is characterized by developments based on intuition, i.e., there was no mathematical background for control design. Maxwell in 1868 [82, 83] and Vyshnegradskii in 1877 [52] set the first
Figure 1.2
Watt’s centrifugal speed regulator.
4
Chapter 1
mathematical background for control design for applying their theoretical (mathematical) results on Watt’s centrifugal regulator. Routh’s mathematical results on stability presented in 1877 [47] were also quite important. Automatic control theory and its applications have developed rapidly in the last 60 years or so. The period 1930–1940 was important in the history of control, since remarkable theoretical and practical results, such as those of Nyquist [84, 85] and Black [60, 61], were reported. During the following years and until about 1960, further significant research and development was reported, due mainly to Ziegler and Nichols [92], Bode [11], Wiener [53] and Evans [18, 64]. All the results of the last century, and up to about 1960, constitute what has been termed classical control. Progress from 1960 to date has been especially impressive, from both the theoretical and the practical point of view. This last period has been characterized as that of modern control, the most significant results of which have been due to Astrom [3–5], Athans [6, 57–59], Bellman [7, 8], Brockett [12, 62], Doyle [63, 66], Francis [63, 66], Jury [24, 25], Kailath [26, 67], Kalman [27, 28, 68–79], Luenberger [33, 80, 81], MacFarlane [34], Rosenbrock [45, 46], Saridis [48], Wonham [54, 89, 90], Wolovich [55], Zames [91], and many others. For more on the historical development of control the reader can refer to [35] and [41]. A significant boost to the development of classical control methods was given by the Second World War, whereas for modern control techniques the launch of Sputnik in 1957 by the former Soviet Union and the American Apollo project, which put men on the moon in 1969, were prime movers. In recent years, an impressive development in control systems has taken place with the ready availability of digital computers. Their power and flexibility have made it possible to control complex systems efficiently, using techniques which were hitherto unknown. The main differences between the classical and the modern control approaches are the following: classical control refers mainly to single input–single output systems. The design methods are usually graphical (e.g., root locus, Bode and Nyquist diagrams, etc.) and hence they do not require advanced mathematics. Modern control refers to complex multi-input multi-output systems. The design methods are usually analytical and require advanced mathematics. In today’s technological control applications, both classical and modern design methods are used. Since classical control is relatively easier to apply than modern control, a control engineer may adopt the following general approach: simple cases, where the design specifications are not very demanding, he uses classical control techniques, while in cases where the design specifications are very demanding, he uses modern control techniques. Today, automatic control systems is a very important area of scientific research and technological development. Worldwide, a large number of researchers aim to develop new control techniques and apply them to as many fields of human activity as possible. In Sec. 1.4, as in other parts of this book, we present many practical control examples that reflect the development of modern control engineering. 1.3
THE BASIC STRUCTURE OF A CONTROL SYSTEM
A system is a combination of components (appropriately connected to each other) that act together in order to perform a certain task. For a system to perform a certain task, it must be excited by a proper input signal. Figure 1.3 gives a simple
Automatic Control Systems
Figure 1.3
5
Schematic diagram of a system with its input and output.
view of this concept, along with the scientific terms and symbols. Note that the response yðtÞ is also called system’s behavior or performance. Symbolically, the outputyðtÞ is related to the input u(t) by the following equation yðtÞ ¼ TuðtÞ
ð1:3-1Þ
where T is an operator. There are three elements involved in Eq. (1.3-1): the input uðtÞ, the system T, and the output yðtÞ. In most engineering problems, we usually know (i.e., we are given) two of these three elements and we are asked to find the third one. As a result, the following three basic engineering problems arise: 1. 2. 3.
The analysis problem. Here, we are given the input uðtÞ and the system T and we are asked to determine the output yðtÞ. The synthesis problem. Here, we are given the input uðtÞ and the output yðtÞ and we are asked to design the system T. The measurement problem. Here, we are given the system T and the output yðtÞ and we are asked to measure the input uðtÞ.
The control design problem does not belong to any of these three problems and is defined as follows. Definition 1.3.1 Given the system T under control and its desired response yðtÞ, find an appropriate input signal uðtÞ, such that, when this signal is applied to system T, the output of the system to be the desired response yðtÞ. Here, this appropriate input signal uðtÞ is called control signal. From Definition 1.3.1 it appears that the control design problem is a signal synthesis problem: namely, the synthesis of the control signal uðtÞ. However, as it will be shown later in this section, in practice, the control design problem is reduced to that of designing a controller (see Definition 1.3.4). Control systems can be divided into two categories: the open-loop and the closed-loop systems. Definition 1.3.2 An open-loop system (Figure 1.4a) is a system whose input uðtÞ does not depend on the output yðtÞ, i.e., uðtÞ is not a function of yðtÞ. Definition 1.3.3 A closed-loop system (Figure 1.4b) is a system whose input uðtÞ depends on the output yðtÞ, i.e., uðtÞ is a function of yðtÞ. In control systems, the control signal uðtÞ is not the output of a signal generator, but the output of another new additional component that we add to the
6
Figure 1.4
Chapter 1
Two types of systems: (a) open-loop system; (b) closed-loop system.
system under control. This new component is called controller (and in special cases regulator or compensator). Furthermore, in control systems, the controller is excited by an external signal rðtÞ, which is called the reference or command signal. This reference signal rðtÞ specifies the desired performance (i.e., the desired ouput yðtÞ) of the open- or closed-loop system. That is, in control systems, we aim to design an appropriate controller such that the output yðtÞ follows the command signal rðtÞ as close as possible. In particular, in open-loop systems (Figure 1.4a) the controller is excited only by the reference signal rðtÞ and it is designed such that its output uðtÞ is the appropriate input signal to the system under control, which in turn will produce the desired output yðtÞ. In closed-loop systems (Figure 1.4b), the controller is excited not only by reference signal rðtÞ but also by the output yðtÞ. Therefore, in this case the control signal uðtÞ depends on both rðtÞ and yðtÞ. To facilitate better understanding of the operation of open-loop and closed-loop systems we present the following introductory examples. A very simple introductory example of an open-loop system is that of the clothes washing machine (Figure 1.5). Here, the reference signal rðtÞ designates the various operating conditions that we set on the ‘‘programmer,’’ such as water temperature, duration of various washing cycles, duration of clothes wringing, etc. These operating conditions are carefully chosen so as to achieve satisfactory clothes washing. The controller is the ‘‘programmer,’’ whose output uðtÞ is the control signal. This control signal is the input to the washing machine and forces the washing machine to execute the desired operations preassigned in the reference signal rðtÞ, i.e., water heating, water changing, clothes wringing, etc. The output of the system yðtÞ is the ‘‘quality’’ of washing, i.e., how well the clothes have been washed. It is well known that during the operation of the washing machine, the output (i.e., whether the
Figure 1.5
The clothes washing machine as an open-loop system.
Automatic Control Systems
7
clothes are well washed or not) it not taken into consideration. The washing machine performs only a series of operations contained in uðtÞ without being influenced at all by yðtÞ. It is clear that here uðtÞ is not a function of yðtÞ and, therefore, the washing machine is a typical example of an open-loop system. Other examples of open-loop systems are the electric stove, the alarm clock, the elevator, the traffic lights, the worldwide telephone communication system, the computer, and the Internet. A very simple introductory example of a closed-loop system is that of the water heater (Figure 1.6). Here, the system is the water heater and the output yðtÞ is the water temperature. The reference signal rðtÞ designates the desired range of the water temperature. Let this desired temperature lie in the range from 65 to 708C. In this example, the water is heated by electric power, i.e., by a resistor that is supplied by an electric current. The controller of the system is a thermostat, which works as a switch as follows: when the temperature of the water reaches 708C, the switch opens and the electric supply is interrupted. As a result, the water temperature starts falling and when it reaches 658C, the switch closes and the electric supply is back on again. Subsequently, the water temperature rises again to 708C, the switch opens again, and so on. This procedure is continuously repeated, keeping the temperature of the water in the desired temperature range, i.e., between 65 and 708C. A careful examination of the water heater example shows that the controller (the thermostat) provides the appropriate input uðtÞ to the water heater. Clearly, this input uðtÞ is decisively affected by the output yðtÞ, i.e., uðtÞ is a function of not only of rðtÞ but also of yðtÞ. Therefore, here we have a typical example of a closed-loop system. Other examples of closed-loop systems are the refrigerator, the voltage control system, the liquid-level control system, the position regulator, the speed regulator, the nuclear reactor control system, the robot, and the guided aircraft. All these closed-loop systems operate by the same principles as the water heater presented above. It is remarked that in cases where a system is not entirely automated, man may act as the controller or as part of the controller, as for example in driving, walking, and cooking. In driving, the car is the system and the system’s output is the course and/or the speed of the car. The driver controls the behavior of the car and reacts accordingly: he steps on the accelerator if the car is going too slow or turns the steering wheel if he wants to go left or right. Therefore, one may argue that driving a car has the structure of a closed-loop system, where the driver is the controller. Similar remarks hold when we walk. When we cook, we check the food in the oven and appropriately adjust the heat intensity. In this case, the cook is the controller of the closed-loop system.
Figure 1.6
The water heater as a closed-loop system.
8
Chapter 1
From the above examples it is obvious that closed-loop systems differ from open-loop systems, the difference being whether or not information concerning the system’s output is fed back to the system’s input. This action is called feedback and plays the most fundamental role in automatic control systems. Indeed, it is of paramount importance to point out that in open-loop systems, if the performance of the system (i.e., yðtÞ) is not satisfactory, the controller (due to the lack of feedback action) does nothing to improve it. On the contrary, in closedloop systems the controller (thanks to the feedback action) acts in such a way as to keep the performance of the system within satisfactory limits. Closed-loop systems are mostly used when the control specifications are highly demanding (in accuracy, in speed, etc.), while open-loop systems are used in simple control problems. Closed-loop systems are, in almost all cases, more difficult to design and implement than open-loop systems. More specific comparisons between open- and closed-loop systems are made in several parts of teh book (e.g., see Sec. 4.5). The complexity in implementing controllers for open- or closed-loop systems increases as the design requirements increase. We can have simple controllers, e.g., thermostats or programmers, but we can also have more complex controllers like an amplifier and/or an RC or an RL network to control a system or process, a computer to control an airplane, or even a number of computers (a computer centre) to control the landing of a spacecraft on Mars. Furthermore, depending mainly upon the design requirements and the nature of the system under control, a controller may be electronic, electrical, mechanical, pneumatic, or hydraulic, or a combination of two or more of these types of controllers. On the basis of all the above material, we can now give the well-known definition of the control design problem. Definition 1.3.4 Given the system T under control and the desired response yðtÞ, find a controller whose output uðtÞ is such that, when applied to system T, the output of the system is the desired response yðtÞ. It is obvious that Definitions 1.3.1 and 1.3.4 are equivalent. In practice, only Definition 1.3.4 is used, which reduces the control problem to that of designing a controller. Many controller design methods have been developed that give satisfactory practical results; however, as technology advances, new control design problems appear, which in turn require new research and development techniques. In closing this section, we present a more complete schematic diagram of openand closed-loop systems. Open-loop systems have the structure of Figure 1.7 and closed-loop systems have the structure of Figure 1.8. In both cases, the control problem is to have yðtÞ follow, as close as possible, the reference signal rðtÞ. This is clearly demonstrated in the many practical control systems presented in Sec. 1.4, which follows. The term disturbances refer to changes in the system’s environment or in the system itself, which result in a deviation of the actual system’s output from its desired form. Based on the material presented thus far, it is obvious that when the output of an open-loop system deviates from its desired form due to disturbances, the controller (due to the lack of feedback action) does nothing to bring it back to its desired form. On the contrary, in a closed-loop system, if the output deviates from its desired form due to disturbances, then (thanks to the feedback action) the controller acts in such a way so as to restore the output to its desired form.
Automatic Control Systems
Figure 1.7
1.4
9
Schematic diagram of an open-loop system.
PRACTICAL CONTROL EXAMPLES
In this section we describe several well-known practical control examples (both openand closed-loop systems) that are certainly more complex than those described in Sec. 1.3. These examples give an overall picture of the wide use of control in modern technology. Furthermore, some of these examples show how the principles of control can be used to understand and solve control problems in other fields, such as economics, medicine, politics, and sociology. Some of the examples given below are studied further in Sec. 3.13, as well as in other parts of this book. From the examples that follow, it will become obvious that many control systems are designed in such a way as to control automatically certain variables of the system (e.g., the voltage across an element, the position or velocity of a mass, the temperature of a chamber, etc.). It is remarked that for the special category of control systems where we control a mechanical movement—e.g., the position or velocity of a mass—the term servomechanism is widely used. 1 Position Control System or Position Servomechanism (Figure 1.9) The desired angular position rðtÞ of the steering wheel is the reference input to the system and the angular position yðtÞ of the small gear is the output of the system. Here, the system is designed such that yðtÞ follows rðtÞ as closely as possible. This is accomplished as follows: the angular positions rðtÞ and yðtÞ are transformed into
Figure 1.8
Schematic diagram of a closed-loop system.
10
Chapter 1
Figure 1.9
Position control or position servomechanism. (a) Overall view of the position control system; (b) schematic diagram of the position control system.
voltages by using potentiometers (see Subsec. 3.12.4). The error eðtÞ ¼ rðtÞ yðtÞ between these two voltages is driven into the amplifier. The output of the amplifier excites the system generator motor (see Subsec. 3.12.1). As a result, the motor turns the gears in one or the other direction, depending on the sign of the error eðtÞ, thus reducing (and finally completely eliminating) the error eðtÞ ¼ rðtÞ yðtÞ. This way, the actual output yðtÞ follows the reference input rðtÞ, i.e., yðtÞ ¼ rðtÞ. In figure 1.9b, a schematic diagram of the system is given, where one can clearly understand the role of feedback and of the controller. A similar system is described in more detail in Subsec. 3.13.2. 2 Metal Sheet Thickness Control System (Figure 1.10) The desired thickness rðtÞ is the reference input to the system and the actual thickness yðtÞ of the metal sheet is the otuput of the system. Here, the system is designed such that yðtÞ follows rðtÞ as closely as possible. This is accomplished as follows: the desired thickness is secured by the appropriate choice of the pressure pðtÞ of the cylinders applied to the metal sheet. This pressure is measured indirectly via the thickness meter which measures the thickness yðtÞ. Let bðtÞ be the indication of this meter. Then, when the error eðtÞ ¼ rðtÞ bðtÞ is not zero, where rðtÞ is the desired
Automatic Control Systems
11
Figure 1.10 Metal sheet thickness control system. (a) Overall view of the metal sheet thickness control system; (b) schematic diagram of the metal sheet control system.
thickness, the hydraulic servomotor increases or decreases the pressure pðtÞ of the cylinders and thus the thickness yðtÞ becomes smaller or greater, respectively. This procedure yields the desired result, i.e., the thickness yðtÞ of the metal sheet is, as close as possible, to the desired thickness rðtÞ. In Figure 1.10b, a schematic diagram of the closed-loop system is given. 3 Temperature Control of a Chamber (Figure 1.11) This system is designed so that the temperature of the chamber, which is the system’s output, remains constant. This is accomplished as follows: the temperature of the chamber is being controlled by a bimetallic thermostat, appropriately adjusted to deactivate the circuit of the magnetic valve whenever the chamber temperature is higher than the desired one. The valve closes and the supply of fuel gas into the burner stops. When the temperature of the chamber is lower than the desired temperature, the circuit of the magnetic valve opens and the supply of fuel gas into the
12
Figure 1.11
Chapter 1
Temperature control of a chamber.
burner starts again. This way, the temperature in the chamber remains close to constant. A similar system is described in more detail in Subsection 3.13.5. 4 Liquid-Level Control (Figure 1.12) This system is used in chemical and other industries and is designed such that the height yðtÞ of the surface of a liquid remains constant. This is accomplished as follows: the cork floating on the surface of the liquid is attached to the horizontal surface of the flapper in such a way that when the height yðtÞ increases or decreases, the distance dðtÞ between the end of the nozzle and the vertical surface of the flapper decreases or increases, respectively. When the distance dðtÞ decreases or increases, subsequently the pressure of the compressed air acting upon the surface A of the valve increases or decreases. As a result, the distance qðtÞ between the piston and the base of the container, decreases or increases, respectively. This control system can be considered as a system whose input is the distance dðtÞ and the output is the pressure of the compressed air acting upon the surface A of the valve. This system is actually a pneumatic amplifier, since although changing dðtÞ does not demand a great amount
Figure 1.12
Liquid-level control system.
Automatic Control Systems
13
of pressure, the corresponding pressure on the surface A is indeed very big. Finally, knowing that a decrease or increase in the distance qðtÞ corresponds to a decrease or increase in the height yðtÞ, it is obvious that in this way the liquid level will remain constant. A similar system is described in more detail in Subsec. 3.13.4. 5 Aircraft Wing Control System (Figure 1.13) This system is designed such that the slope (or angle or inclination) of the wings of the aircraft is controlled manually by the pilot using a control stick. The system works as follows: when the control stick is moved to a new position, the position A of the potentiometer P changes, creating a voltage across the points A and B. This voltage activates the electromagnet and the piston of the valve of the hydraulic servomotor is moved (see Subsec. 3.12.6). The movement of the valve will allow the oil under pressure to enter the power cylinder and to push its piston right or left, moving the wings of the aircraft downwards or upwards. This way, the pilot can control the inclination of the wings. 6 Missile Direction Control System (Figure 1.14) This system directs a missile to destroy an enemy aircraft. The system works as follows: the guided missile, as well as its target, are monitored by a radar system. The information acquired by the radar is fed into a computer, which estimates the possible course of the enemy aircraft. The missile’s course changes, as new data of the aircraft’s course is received. The computer constantly compares the two courses and makes the necessary corrections in the direction of the missle so that it strikes the target.
Figure 1.13
Aircraft wing control system.
14
Figure 1.14
Chapter 1
Missile direction control system.
7 Paper-Making Control System (Figure 1.15) This system is designed such that the output yðtÞ of the system, i.e., the consistency of the dilution of the thick pulp, remains constant. This is accomplished as follows: the pulp is stored in a large container, it is constantly rotated by a mixing mechanism to maintain pulp uniformity. Subsequently, as the pulp is driven into the drying and rolling stations of the paper-making industrial plant, water is added, which dilutes the thick pulp to a desired consistency rðtÞ. The actual consistency yðtÞ is measured by an appropriate device and is compared with the desired consistency rðtÞ. The controller compares rðtÞ and yðtÞ. If yðtÞ 6¼ rðtÞ, then the output of the controller uðtÞ adjusts the water valve such that yðtÞ ¼ rðtÞ. 8 Nuclear Reactor Control System (Figure 1.16) The major control objective of a nuclear reactor is to maintain the output power within specified limits. This can be achieved as follows. The nuclear reaction releases energy in the form of heat. This energy is used for the production of steam. The steam is subsequently used to drive a turbine and, in turn, the turbine drives a generator, which finally produces electric power. The reference signal rðtÞ corresponds to the desired output power, whereas yðtÞ is the actual output power. The two signals rðtÞ and yðtÞ are compared and their
Automatic Control Systems
Figure 1.15
15
Paper-making control system.
difference eðtÞ ¼ rðtÞ yðtÞ is fed into the control unit. The control unit consists of special rods which, when they move towards the point where the nuclear reaction takes place, result in an increase of the output power yðtÞ, and when they move away, result in a decrease of the output power yðtÞ. When yðtÞ > rðtÞ, the error eðtÞ is negative, the rods move away from the point of the nuclear reaction and the output yðtÞ decreases. When yðtÞ < rðtÞ, the error eðtÞ is positive, the rods move towards the point of the nuclear reaction and the output yðtÞ increases. This way, the power output yðtÞ follows the desired value rðtÞ. 9 Boiler–Generator Control System (Figure 1.17) The boiler–generator control system operates as follows: the steam produced by the boiler sets the shaft in rotation. As the shaft rotates, the generator produces electric power. Here, we have a system with many inputs (water, air, and liquid fuel) and one output (electric power). The electric power is automatically controlled as follows: the output power, along with intermediate variables or states of the system, such as oxygen, temperature, and pressure, are fed back to the controller, namely, to the computer. The computer regulates automatically the amount of water, air, and liquid fuel that should enter the boiler, as well as the angular velocity of the shaft, depending on the desired and real (measured) values of temperature, pressure, oxygen, and electric power, such that the electric power output is the desired one. Clearly, the controller here is the digital computer. Systems that are controlled by a computer are usually called computer-controlled systems or digital control systems and they are studied in Chapter 12. 10 Remote Robot Control (Figure 1.18) Here, we consider a remote control system that can control, from the earth, the motion of a robot arm on the surface of the moon. As shown in Figure 1.18a, the operator at earth station watches the robot on the moon on a TV monitor. The system’s output is the position of the robot’s arm and the input is the position of the control stick. The operator compares the desired and the real position of the robot’s arm, by looking at the position of the robot’s arm on the monitor and decides on how to move the control stick so that the position of the robot arm is the desired one.
16
Chapter 1
Figure 1.16
Control system of a nuclear reactor. (a) Overall picture of the nuclear reactor; (b) schematic diagram of the system.
In Figure 1.18b a schematic diagram of this system is given. In this example, the operator is part of the controller. 11 Machine Tool Control (Figure 1.19) A simplified scheme of a machine tool for cutting (or shaping or engraving) metals is shown in Figure 1.19. The motion of the cutting tool is controlled by a computer. This type of control is termed numerical control. When there is a difference between the desired position rðtÞ and the actual position yðtÞ of the cutting tool, the amplifier amplifies this difference so that the output current of the amplifier is large enough to
Automatic Control Systems
Figure 1.17
17
Boiler–generator control system [16].
activate the coil. The magnetic field produced around the coil creates a force on the piston of the valve of the hydraulic servomotor, moving it to the left or to the right. These small movements of the piston result in controlling the position of the cutting tool in such a way that yðtÞ ¼ rðtÞ. 12 Ship Stabilization (Figure 1.20) This example refers to the stabilization of ship oscillations due to waves and strong winds. When a ship exhibits a deviation of 8 from the vertical axis, as shown in Figure 1.20, then most ships use fins to generate an opposite torque, which restores the ship to the vertical position. In Figure 1.20b the block diagram of the system is given, where, obviously, r ðtÞ ¼ 0 is the desired position of the ship. The length of the fins projecting into the water is controlled by an actuator. The deviation from the vertical axis is measured by a measuring device. Clearly, when the error eðtÞ ¼ r ðtÞ y ðtÞ 6¼ 0, then the fin actuator generates the proper torque, such that the error eðtÞ goes to zero, i.e., the ship position is restored to normal ðr ðtÞ ¼ 0Þ. 13 Orientation Control of a Sun-Seeker System (Figure 1.21) The sun-seeker automatic control system is composed of a telescope, two lightsensing cells, an amplifier, a motor, and gears. The two light-sensing cells are placed on the telescope in such a way that when the telescope is not aligned with the sun, one cell receives more light than the other. The two cells behave as current sources and are conected with opposite polarity, so that when one of the
18
Chapter 1
Figure 1.18
Remote robot control system. (a) Overall picture of remote robot control system; (b) schematic diagram of remote robot control.
Figure 1.19
Machine tool control [16].
Automatic Control Systems
19
(a)
(b)
Figure 1.20 Ship stabilization control system. (a) Ship in roll position; (b) simplified block diagram of ship stabilization control system.
two cells gets more light, a current Is is produced, which is equal to the difference of the two currents of the cells. This current is subsequently driven into the amplifier. The output of the amplifier is the input to the motor, which in turn moves the gears in such a way as to align the telescope with the sun, i.e., such that y ðtÞ ¼ r ðtÞ, where r ðtÞ is the desired telescope angle and y ðtÞ is the actual telescope angle. 14 Laser Eye Surgery Control System (Figure 1.22) Lasers can be used to ‘‘weld’’ the retina of the eye in its proper position inside the eye in cases where the retina has been detached from its original place. The control scheme shown in Figure 1.22 is of great assistance to the ophthalmologist during surgery, since the controller continuously monitors the retina (using a wide-angle video camera system) and controls the laser’s position so that each lesion of the retina is placed in its proper position.
20
Figure 1.21
Chapter 1
Orientation control of a sun-seeker system [31].
15 Wheelchair The automatic wheelchair is especially designed for people disabled from their neck down. It is actually a system which the disabled person activates by moving his head. In so doing, he determines both the direction and the speed of the wheelchair. The direction is determined by sensors placed on the person’s head 908 apart, so that he may choose one of the following four movements: forward, backward, left, or right. The speed is determined by another sensor whose output is proportional to the speed of the head movement. Clearly, here, the man is the controller. 16 Economic Systems (Figure 1.23) The concept of closed-loop control systems also appears in economic and social systems. As an example, consider the inflation control system presented in Figure
Figure 1.22
Laser eye surgery control system.
Automatic Control Systems
Figure 1.23
21
Schematic diagram of inflation control system.
1.23. Here, the input rðtÞ to the system is the desired level of inflation. The system under control is the society, and yðtÞ is the actual inflation. The government operates as a controller, comparing the desired inflation rðtÞ and the actual inflation yðtÞ. If yðtÞ rðtÞ, no action takes place. If yðtÞ > rðtÞ, the controller (i.e., the government) takes the necessary decisions so as to keep yðtÞ rðtÞ. The same closed-loop scheme may be used to describe a variety of economic systems, as for example unemployment and national income. In example 4.8.1, the operation of a company is also described as a closed-loop system, wherein its mathematical model is used to study the performance of the company. 17 Human Speech (Figure 1.24) As we all know, we use our ears not only to hear others but also to hear ourselves. Indeed, when we speak, we hear what we are saying and, if we realize that we didn’t say something the way we had in mind to say it, we immediately correct it. Thus, human speech operates as a closed-loop system, where the reference input rðtÞ is what we have in mind to say and want to put into words, the system is the vocal cords, and its output yðtÞ is our voice. The output yðtÞ is continuously monitored by our ears, which feed back our voice to our brain, where comparison is made between ourintended (desired) speech rðtÞ and the actual speech yðtÞ that our own ears hear (measure). If the desired speech rðtÞ and the ‘‘measured’’ speech yðtÞ are the same, no correction is necessary, and we keep on talking. If, however, an error is realized, e.g., in a word or in a number, then we immediately make the correction by saying
Figure 1.24
Block diagram of human speech.
22
Figure 1.25
Chapter 1
Schematic diagram of teaching.
the correct word or number. The reason that some people talk very loud is that their hearing is not very good and, in order to be able to hear themselves (so as to make the necessary speech corrections), they talk louder than normal. 18 Teaching (Figure 1.25) The proper procedure for teaching has the structure of a closed-loop system. Let the students be the system, the teaching material presented by the teacher the input, and the ‘‘degree’’ of understanding of this material by the students the system’s output. Then, teaching can be described with the schematic diagram of Figure 1.25. This figure shows that the system’s output, i.e., the degree of understanding by students of the material taught, is fed back to the input, i.e., to the teacher. Indeed, an experienced teacher should be able to ‘‘sense’’ (measure) if the students understood the material taught. Subsequently, the teacher will either go on teaching new material, if the students understood the material taught, or repeat the same material, if they did not. Therefore, proper teaching has indeed the structure of a closed-loop system. Clearly, if teachers keep on teaching new material without checking whether or not the students understand what they are saying, this is not proper teaching. BIBLIOGRAPHY Books 1. 2. 3.
J Ackermann. Sampled Data Control Systems. New York: Springer Verlag, 1985. PJ Antsaklis, AN Michel. Linear Systems. New York: McGraw-Hill, 1997. KJ Astrom. Introduction to Stochastic Control Theory. New York: Academic Press, 1970. 4. KJ Astrom, B Wittenmark. Computer Controlled Systems: Theory and Design. 3rd edn. Englwood Cliffs, New Jersey: Prentice Hall, 1997. 5. KJ Astrom, B Wittenmark. Adaptive Control. 2nd edn. New York: Addison-Wesley, 1995. 6. M Athans, PL Falb. Optimal Control. New York: McGraw-Hill, 1966. 7. R Bellman. Dynamic Programming. Princeton, New Jersey: Princeton University Press, 1957. 8. R Bellman. Adaptive Control Processes, A Guided Tour. Princeton, New Jersey: Princeton University Press, 1961. 9. S Bennet. Real-Time Computer Control, An Introduction. New York: Prentice Hall, 1988. 10. J Billingsley. Controlling with Computers. New York: McGraw-Hill, 1989.
Automatic Control Systems 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.
28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43.
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HW Bode. Network Analysis and Feedback Amplifier Design. New York: Van Nostrand, 1945. RW Brockett. Finite Dimensional Linear Systems. New York: John Wiley, 1970. JA Cadzow. Discrete-Time Systems, An Introduction with Interdisciplinary Applications. Englewood Cliffs, New Jersey: Prentice Hall, 1973. CT Chen. Linear System Theory and Design. New York: Holt, Rinehart and Winston, 1984. JJ D’Azzo, CH Houpis. Linear Control System Analysis and Design, Conventional and Modern. New York: McGraw-Hill, 1975. RC Dorf, RE Bishop. Modern Control Analysis. London: Addison-Wesley, 1995. JC Doyle, BA Francis, A Tannenbaum. Feedback Control theory. New York: Macmillan, 1992. WR Evans. Control Systems Dynamics. New York: McGraw-Hill, 1954. GF Franklin, JD Powell, A Emami-Naeini. Feedback Control of Dynamic Systems. Reading, MA: Addison-Wesley, 1986. GF Franklin, JD Powell, ML Workman. Digital Control of Dynamic Systems. 2nd edn. London: Addison-Wesley, 1990. M Santina, A Stubbersud, G Hostetter. Digital Control System Design. Orlando, Florida: Saunders College Publishing, 1994. CH Houpis, GB Lamont. Digital Control Systems. New York: McGraw-Hill, 1985. R Iserman. Digital Control Systems, Vols I and II. Berlin: Springer Verlag, 1989. EI Jury. Theory and Application of the Z-Transform Method. New York: John Wiley, 1964. EI Jury. Sampled Data Control Systems. New York: John Wiley, 1958. Huntington, New York: Robert E Krieger, 1973 (2nd edn). T Kailath. Linear Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1980. RE Kalman. The theory of optimal control and the calculus of variations. In: R Bellman, ed. Mathematical Optimization Techniques, Berkeley, California: University of California Press, 1963. RE Kalman, PL Falb, MA Arbib. Topics in Mathematical System Theory. New York: McGraw-Hill, 1969. P Katz. Digital Control Using Microprocessors. London: Prentice Hall, 1981. V Kucera. Discrete Linear Control, The Polynomial Equation Approach. New York: John Wiley, 1979. BC Kuo. Automatic Control Systems. London: Prentice Hall, 1995. BC Kuo. Digital Control Systems. Orlando, Florida: Saunders College Publishing, 1992. DG Luenberger. Optimatization by Vector Space Methods. New York: John Wiley, 1969. AGC MacFarlane. Dynamic Systems Models. London: George H Harrap, 1970. O Mayr. The Origins of Feedback Control. Cambridge, Massachusetts: MIT Press, 1970. RH Middleton, GC Goodwin. Digital Control and Estimation: A Unifieid Approach. New York: Prentice Hall, 1990. G Newton, L Gould, J Kaiser. Analytical Design of Linear Feedback Controls. New York: John Wiley & Sons, 1957. NS Nise. Control Systems Engineering. New York: Benjamin and Cummings, 1995. K Ogata. Modern Control Systems. London: Prentice-Hall, 1997. K Ogata. Discrete-Time Control Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1987. M Otto. Origins of Feedback Control. Cambridge, Massachusetts: MIT Press, 1971. PN Paraskevopoulos. Digital Control Systems. London: Prentice Hall, 1996. CL Phillips, HT Nagle Jr. Digital Control Systems Analysis and Design. Englewood Cliffs, New Jersey, 1984.
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JR Ragazzini, GF Franklin. Sampled Data Control Systems. New York: McGraw-Hill, 1958. HH Rosenbrock. State Space and Multivariable Theory. London: Nelson, 1970. HH Rosenbrock. Computer Aided Control System Design. New York: Academic Press, 1974. EJ Routh. A Treatise on the Stability of a Given State of Motion. London: Macmillan, 1877. GN Saridis. Self-Organizing Control of Stochastic Systems. New York: Marcel Dekker, 1977. V Strejc. State Space Theory of Discrete Linear Control. New York: Academia Prague and John Wiley, 1981. GJ Thaler. Automatic Control Systems. St. Paul, Minn.: West Publishing, 1989. AIG Vardulakis. Linear Multivariable Control. Algebraic Analysis and Synthesis Methods. New York: Wiley, 1991. IA Vyshnegradskii. On Controllers of Direct Action. Izv.SPB Teckhnolog. Inst., 1877. N Wiener. The Extrapolation, Interpolation and Smoothing of Stationary Time Series. New York: John Wiley, 1949. WM Wohnam. Linear Multivariable Control: A Geometric Approach. 2nd edn. New York: Springer Verlag, 1979. WA Wolovich. Linear Multivariable Systems. New York: Springer Verlag, 1974. LA Zadeh, CA Desoer. Linear System Theory – The State Space Approach. New York: McGraw-Hill, 1963.
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M Athans. Status of optimal control theory and applications for deterministic systems. IEEE Trans Automatic control AC-11:580–596, 1966. M Athans. The matrix minimum principle. Information and Control 11:592–606, 1968. M Athans. The role and use of the stochastic linear quadratic-gaussian problem in control system design. IEEE Trans Automatic Control AC-16:529–551, 1971. HS Black. Stabilized feedback amplifiers. Bell System Technical Journal, 1934. HS Black. Inverting the negative feedback amplifier. IEEE Spectrum 14:54–60, 1977. RW Brockett. Poles, zeros and feedback: state space interpretation. IEEE Trans Automatic control AC-10:129–135, 1965. JC Doyle, K Glover, PP Khargonekar, BA Francis. State space solutions to standard H2 and H1 control problems. IEEE Trans Automatic Control 34:831–847, 1989. WR Evans. Control system synthesis by root locus method. AIEE Trans, Part II, 69:66– 69, 1950. AT Fuller. The early development of control theory. Trans ASME (J Dynamic Systems, Measurement & Control) 96G:109–118, 1976. BA Francis, JC Doyle. Linear control theory with an H1 optimally criterion. SIAM J Control and Optimization 25:815–844, 1987. T Kailath. A view of three decades of linear filtering theory. IEEE Trans Information Theory 20:146–181, 1974. RE Kalman. A new approach to linear filtering and prediction problems. Trans ASME (J Basic engineering) 82D:35–45, 1960. RE Kalman. On the general theory of control systems. Proceedings First International Congress, IFAC, Moscow, USSR, 1960, pp 481–492. RE Kalman. Contributions to the theory of optimal control. Proceedings 1959 Mexico City Conference on Differential Equations, Mexico City, 1960, pp 102–119. RE Kalman. Canonical structure of linear dynamical systems. Proceedings National Academy of Science 48:596–600, 1962.
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RE Kalman. Mathematical description of linear dynamic systems. SIAM J Control 1:152–192, 1963. RE Kalman. When is a linear control system optimal? Trans ASME (J Basic Engineering) 86D:51–60, 1964. RE Kalman, JE Bertram. A unified approach to the theory of sampling systems. J Franklin Institute 405–524, 1959. RE Kalman, JE Bertram. Control systems analysis and design via second method of Liapunov: II discrete-time systems. Trans ASME (J Basic Engineering) D:371–400, 1960. RE Kalman, RS Bucy. New results in linear filtering and prediction theory. Trans ASME (J Basic Engineering) 83D:95–108, 1961. RE Kalman, RW Koepcke. Optimal synthesis of linear sampling control systems using generalized performance indices. Trans ASME 80:1820–1826, 1958. RE Kalman, YC Ho, KS Narenda. Controllability of linear dynamic systems. Contributions to Differential Equations 1:189–213, 1963. RE Kalman et al. Fundamental study of adaptive control systems. Wright-Patternson Air Force Base Technical Report, ASD-TR-61-27, April 1962. DG Luenberger. Observingt the state of a linear system. IEEE Trans Military Electronics MIL-8:74–80, 1964. DG Luenberger. An introduction to obsevers. IEEE Trans Automatic Control AC16:596–602, 1971. JC Maxwell. On governors. Proceedings of the Royal Society of London Vol. 16, 1868. See also Selected Papers on Mathematical Trends in Control Theory. New York: Dover, 1964, pp 270–283. JC Maxwell. On governors. Philosophical Magazine 35:385–398, 1868. H Nyquist. Certain topics in telegraph transmission theory. Trans AIEE 47:617–644, 1928. H Nyquist. Regeneration theory. Bell System Technical J 11:126–147, 1932. Special issue on linear-quadratic-gaussian problem. IEEE Trans Automatic Control AC16: December 1971. Special issue on identification and system parameter estimation. Automatica 17, 1981. Special issue on linear multivariable control systems. IEEE Trans on Automatic Control AC-26, 1981. WM Wonham. On pole assignment in multi-input controllable systems. IEEE Trans Automatic Control AC-12:660–665, 1967. WM Wonham. On the separation theorem of stochastic control. SIAM J Control 6:312– 326, 1968. G Zames. Feedback and optimal sensitivity: model reference transformations, multiplicative seminorms and approximate inverses. IEEE Trans Automatic Control 26:301–320, 1981. JG Ziegler, NB Nichols. Optimum settings for automatic controllers. Trans ASME 64:759–768, 1942.
2 Mathematical Background
2.1
INTRODUCTION
This chapter covers certain mathematical topics necessary for the study of control systems: in particular, it aims to offer the appropriate mathematical background on subjects such as basic control signals, Laplace transform, and the theory of matrices. This background is very useful for most of the material of the book that follows. 2.2
THE BASIC CONTROL SIGNALS
In this section we present definitions of the following basic control signals: the step function, the gate function, the impulse function, the ramp function, the exponential function, and the sinusoidal function. These signals are of major importance for control applications. 1 The Unit Step Function The unit step function is designated by uðt TÞ and is defined as follows: 2 1 for t > T uðt TÞ ¼ 4 0 for t < T undefined for t ¼ T
ð2:2-1Þ
The graphical representation of uðt TÞ is shown in Figure 2.1. The amplitude of uðt TÞ, for t > T, is equal to 1. This is why the function uðt TÞ is called the ‘‘unit’’ step function. A physical example of a unit step function is the switch of the circuit shown in Figure 2.2. It is obvious that the voltage vR ðtÞ is given by: 2 vðtÞ for t > T vR ðtÞ ¼ 4 0 for t < T undefined for t ¼ T or vR ðtÞ ¼ vðtÞuðt TÞ 27
28
Figure 2.1
Chapter 2
The unit step function.
Here, the role of the switch is expressed by the unit step function uðt TÞ. 2 The Unit Gate Function The unit gate (or window) function is denoted by g ðtÞ ¼ uðt T1 Þ uðt T2 Þ, where T1 < T2 , and is defined as follows: 2 1 for t 2 ðT1 ; T2 Þ ð2:2-2Þ for t 6¼ ðT1 ; T2 Þ g ðtÞ ¼ 4 0 undefined for t ¼ T1 and t ¼ T2 The graphical representation of g ðtÞ is given in Figure 2.3. The unit gate function is usually used to zero all values of another function, outside a certain time interval. Consider for example the function f ðtÞ. Then, the function yðtÞ ¼ f ðtÞg ðtÞ is as follows: 2 f ðtÞ for T1 t T2 yðtÞ ¼ f ðtÞg ðtÞ ¼ 4 0 for t < T1 and for t > T2 3 The Unit Impulse Function The unit impulse function, which is also called the Dirac function, is designated by ðt TÞ and is defined as follows: 0 8t; except for t ¼ T ð2:2-3Þ ðt TÞ ¼ 1 for t ¼ T
Figure 2.2
The switch as the unit step function.
Mathematical Background
Figure 2.3
29
The unit gate function.
The graphical representation of ðt TÞ is given in Figure 2.4. In Figure 2.5 ðt TÞ is defined in a different way as follows: the area cðtÞ of the parallelogram is cðtÞ ¼ ð1=aÞa ¼ 1. As a becomes larger, the base of the parallelogram 1=a becomes smaller. In the limit, as the height a tends to infinity, the base 1=a tends to zero, i.e., ðt TÞ occurs when lim cðtÞ a!1
From definition (2.2-4) we readily have ð1 ðt TÞ dt ¼ 1
ð2:2-4Þ
ð2:2-5Þ
1
Relation (2.2-5) shows that the area of the unit impulse function is equal to 1 (this is why it is called the ‘‘unit’’ impulse function). The functions uðt TÞ and ðt TÞ are related as follows: ðt duðt TÞ and uðt TÞ ¼ ð TÞ d ð2:2-6Þ ðt TÞ ¼ dt 1 Finally, we have the following interesting property of ðt TÞ: consider a function xðtÞ with the property jxðtÞj < 1, then ð1 xðtÞðt TÞ dt ¼ xðTÞ ð2:2-7Þ 1
4 The Ramp Function The ramp function is designated by rðt TÞ and is defined as follows: tT for t > T rðt TÞ ¼ 0 for t T
Figure 2.4
The unit impulse function.
ð2:2-8Þ
30
Figure 2.5
Chapter 2
The area function cðtÞ.
The graphical representation of rðt TÞ is shown in Figure 2.6. It is obvious that uðt TÞ and rðt TÞ are related as follows: ðt drðt TÞ uðt TÞ ¼ uð TÞ d and rðt TÞ ¼ dt 1 Remark 2.2.1 All the above functions are usually applied when T ¼ 0. In cases where T > 0, then the function is delayed by T units of time, whereas when T < 0 the function is preceding by T units of time. 5 The Exponential Function The exponential function is the function f ðtÞ ¼ Aeat and its graphical representation is shown in Figure 2.7. 6 The Sinusoidal Function The sinsoidal function is the function f ðtÞ ¼ A sinð!t þ Þ and its graphical representation is shown in Figure 2.8. Remark 2.2.2 All functions presented in this section can be expressed in terms of exponential functions or derived from the exponential function, a fact which makes the exponential function very interesting. This can easily be shown as follows: (a) the sinusoidal function is a linear combination of two exponential functions, e.g., sin ¼ ð1=2jÞðej ej Þ; (b) the unit step function for T ¼ 0 is equal to the exponential function when A ¼ 1 and a ¼ 0, i.e., uðtÞ ¼ f ðtÞ ¼ Aeat ¼ 1, for A ¼ 1 and a ¼ 0; (c) the functions ðt TÞ and rðt TÞ can be derived from the unit step
Figure 2.6
The ramp function.
Mathematical Background
Figure 2.7
31
The exponential function.
function uðt TÞ, while uðt TÞ may be derived from the exponential function. Furthermore, a periodic function can be expressed as a linear combination of exponential functions (Fourier series). Moreover, it is worth mentioning that the exponential function is used to describe many physical phenomena, such as the response of a system and radiation of nuclear isotopes.
2.3
THE LAPLACE TRANSFORM
To study and design control systems, one relies to a great extent on a set of mathematical tools. These mathematical tools, an example of which is the Laplace transform, facilitate the engineer’s work in understanding the problems he deals with as well as solving them. For the special case of linear time-invariant continuous time systems, which is the main subject of the book, the Laplace transform is a very important mathematical tool for the study and design of such systems. The Laplace transform is a special case of the generalized integral transform presented just below.
Figure 2.8
The sinusoidal function.
32
2.3.1
Chapter 2
The Generalized Linear Integral Transform
The generalized linear integral transform of a function f ðtÞ is defined as follows: ðb FðsÞ ¼ f ðtÞkðs; tÞ dt ð2:3-1Þ a
where kðs; tÞ is known as the kernel of the transform. It is clear that the main feature of Eq. (2.3-1) is that it transforms a function defined in the t domain to a function defined in the s domain. A particular kernel kðs; tÞ, together with a particular time interval ða; bÞ, define a specific transform. 2.3.2
Introduction to Laplace Transform
The Laplace transform is a linear integral transform with kernel kðs; tÞ ¼ est and time interval ð0; 1Þ. Therefore, the definition of the Laplace transform of a function f ðtÞ is as follows: ð1 Lf f ðtÞg ¼ f ðtÞest dt ¼ FðsÞ ð2:3-2Þ 0
where L designates the Laplace transform and s is the complex variable defined as s ¼ þ j!. Usually, the time function f ðtÞ is written with a small f , while the complex variable function FðsÞ is written with a capital F. For the integral (2.3-2) to converge, f ðtÞ must satisfy the condition ð1 j f ðtÞjet dt M ð2:3-3Þ 0
where and M are finite positive numbers. Let Lf f ðtÞg ¼ FðsÞ. Then, the inverse Laplace transform of FðsÞ is also a linear integral transform, defined as follows: ð 1 cþj1 FðsÞest ds ¼ f ðtÞ ð2:34Þ L1 fFðsÞg ¼ 2j cj1 pffiffiffiffiffiffiffi where L1 designates the inverse Laplace transform, j ¼ 1, and c is a complex constant. Clearly, the Laplace transform is a mathematical tool which transforms a function from one domain to another. In particular, it transforms a time-domain function to a function in the frequency domain and vice versa. This gives the flexibility to study a function in both the time domain and the frequency domain, which results in a better understanding of the function, its properties, and its time-domain frequency-domain properties. A popular application of the Laplace transform is in solving linear differential equations with constant coefficients. In this case, the motivation for using the Laplace transform is to simplify the solution of the differential equation. Indeed, the Laplace transform greatly simplifies the solution of a constant coefficient differential equation, since it reduces its solution to that of solving a linear algebraic equation. The steps of this impressive simplification are shown in the bottom half of Figure 2.9. These steps are analogous to the steps taken in the case of multiplying numbers using logarithms, as shown in the top half of Figure 2.9. The analogy here is
Mathematical Background
Figure 2.9
33
Comparison of logarithms with the Laplace transform.
that logarithms reduce the multiplication of two numbers to the sum of their logarithms, while the Laplace transform reduces the solution of a differential equation to an algebraic equation. 2.3.3
Properties and Theorems of the Laplace Transform
The most important properties and theorems of the Laplace transform are presented below. 1 Linearity The Laplace transform is a linear transformation, i.e., the following relation holds Lfc1 f1 ðtÞ þ c2 f2 ðtÞg ¼ Lfc1 f1 ðtÞg þ Lfc2 f2 ðtÞg ¼ c1 F1 ðsÞ þ c2 F2 ðsÞ
ð2:3-5Þ
where c1 and c2 are constants, F1 ðsÞ ¼ Lf f1 ðtÞg and F2 ðsÞ ¼ Lf f2 ðtÞg. 2 The Laplace Transform of the Derivative of a Function Let f ð1Þ ðtÞ be the time derivative of f ðtÞ, and FðsÞ be the Laplace transform of f ðtÞ. Then, the Laplace transform of f ð1Þ ðtÞ is given by
34
Chapter 2
n o L f ð1Þ ðtÞ ¼ sFðsÞ f ð0Þ
ð2:3-6Þ
Proof From definition (2.3-2) we have that 1 ð1 ð1 n o ð1 f ð1Þ ðtÞest dt ¼ est df ðtÞ ¼ f ðtÞest þs f ðtÞest dt L f ð1Þ ðtÞ ¼ 0
0
0
0
¼ sFðsÞ f ð0Þ where use was made of the integration-by-parts method. Working in the same way for the Laplace transform of the second derivative f ð2Þ ðtÞ of f ðtÞ, we have that n o L f ð2Þ ðtÞ ¼ s2 FðsÞ sf ð0Þ f ð1Þ ð0Þ ð2:3-7Þ For the general case we have n o L f ðnÞ ðtÞ ¼ sn FðsÞ sn1 f ð0Þ sn2 f ð1Þ ð0Þ . . . f ðn1Þ ð0Þ ¼ sn FðsÞ
n1 X
sk f ðnk1Þ ð0Þ
ð2:3-8Þ
k¼0
where f ðmÞ ðtÞ is the mth time derivative of f ðtÞ. 3 The Laplace Transform of the Integral of a Function Ðt Let f ðÞd be the integral of a function f ðtÞ, where is a positive number and FðsÞ is the Laplace transform of f ðtÞ. Then, the Laplace transform of the integral is given by ðt FðsÞ f ð1Þ ð0Þ þ ð2:3-9Þ L f ðÞ d ¼ s s Ð0 where f ð1Þ ð0Þ ¼ f ðtÞ dt. Proof From definition (2.3-2) we have that ðt ð1 ðt ð ð 1 1 t st L f ðÞ d ¼ f ðÞ d e dt ¼ f ðÞ d d½est s 0 0 ð t 1 ð 1 1 f ðÞ d est est f ðtÞ dt ¼ s 0 0 ð1 ð0 1 1 FðsÞ f ð1Þ ð0Þ þ f ðtÞest dt þ f ðtÞ dt ¼ ¼ s 0 s s s where use was made of the integration-by-parts method. Working in Ð t same way, we may determine the Laplace transform of the Ð t the double integral f ðÞðdÞ2 to yield ðt ðt FðsÞ f ð1Þ ð0Þ f ð2Þ ð0Þ 2 ð2:3-10Þ L f ðÞðdÞ ¼ 2 þ þ s s s2
Mathematical Background
where f ð2Þ ð0Þ ¼
ð0 ð0
35
f ðtÞðdtÞ2 :
For the general case we have 8 9 > > > > > > ðt < ðt = FðsÞ f ð1Þ ð0Þ f ð2Þ ð0Þ f ðnÞ ð0Þ n ¼ n þ ... f ðÞðdÞ þ þ þ L > > s sn s sn1 > > > > : |fflfflfflfflfflfflffl{zfflfflfflfflfflfflffl} ;
ð2:3-11Þ
n times
where f ðkÞ ð0Þ ¼
ð0
ð0 ... f ðtÞðdtÞk |fflfflfflfflfflfflffl{zfflfflfflfflfflfflffl} k times
Remark 2.3.1 In the special case where f ðkÞ ð0Þ ¼ 0, for k ¼ 0; 1; . . . ; n 1 and f ðkÞ ð0Þ ¼ 0, for k ¼ 1; 2; . . . ; n, relations (2.3-8) and (2.3-11) reduce to n o L f ðnÞ ðtÞ ¼ sn FðsÞ ð2:3-12aÞ 9 8 > > > > > > ðt < ðt = FðsÞ n L ... f ðÞðdÞ ¼ n ð2:3-12bÞ > > s > > > > |fflfflfflfflfflffl ffl{zfflfflfflfflfflffl ffl} ; : n times
Relation (2.3-12) points out that the important feature of the Laplace transform is that it greatly simplifies the procedure of taking the derivative and/or the integral of a function f ðtÞ. Indeed, the Laplace transform ‘‘transforms’’ the derivative of f ðtÞ in the time domain into multiplying FðsÞ by s in the frequency domain. Furthermore, it ‘‘transforms’’ the integral of f ðtÞ in the time domain into dividing FðsÞ by s in the frequency domain. 4 Time Scaling Consider the functions f ðtÞ and f ðatÞ, where a is a positive number. The function f ðatÞ differs from f ðtÞ, in time scaling, by a units. For these two functions, it holds that 1 s ; where FðsÞ ¼ Lf f ðtÞg ð2:3-13Þ Lf f ðatÞg ¼ F a a Proof From definition (2.3-2), we have ð ð1 s 1 1 Lf f ðatÞg ¼ f ðatÞest dt ¼ f ðatÞe a ðatÞ dðatÞ a 0 0 Setting ¼ at, we arrive at the relation
36
Chapter 2
Lf f ðatÞg ¼ Lf f ðÞg ¼
1 a
ð1 0
s 1 s f ðÞea d ¼ F a a
5 Shift in the Frequency Domain It holds that
L eat f ðtÞ ¼ Fðs þ aÞ
ð2:3-14Þ
Relation (2.3-14) shows that the Laplace transform of the product of the functions eat and f ðtÞ leads to shifting of FðsÞ ¼ Lf f ðtÞg by a units. Proof From definition (2.3-2) we have ð1 ð1
L eat f ðtÞ ¼ f ðtÞeat est dt ¼ f ðtÞeðsþaÞt dt ¼ Fðs þ aÞ 0
0
6 Shift in the Time Domain Consider the function f ðtÞuðtÞ. Then, the function f ðt TÞuðt TÞ is the same function shifted to the right of f ðtÞuðtÞ by T units (Figure 2.10). The Laplace transform of the initial function f ðtÞuðtÞ and of the shifted (delayed) function f ðt TÞuðt TÞ, are related as follows: Lf f ðt TÞuðt TÞg ¼ esT FðsÞ Proof Setting ¼ t T, we have
ð1 Lf f ðt TÞuðt TÞg ¼ L½ f ðÞuðÞ ¼ f ðÞuðÞesðþTÞ d 0 ð1 sT ¼e f ðÞuðÞes d 0
¼e
Figure 2.10
sT
FðsÞ
Time-delayed function.
ð2:3-15Þ
Mathematical Background
37
7 The Initial Value Theorem This theorem refers to the behavior of the function f ðtÞ as t ! 0 and, for this reason, is called the initial value theorem. This theorem is given by the relation lim f ðtÞ ¼ lim sFðsÞ t!0
ð2:3-16Þ
s!1
assuming that the Laplace transform of f ð1Þ ðtÞ exists. Proof Taking the limit of both sides of Eq. (2.3-6) as s ! 1, the left-hand side of this relation becomes ð1 n o ð1Þ f ð1Þ ðtÞest dt ¼ 0 lim L f ðtÞ ¼ lim s!1
s!1 0
while the right-hand side becomes lim ½sFðsÞ f ð0Þ ¼ 0
and hence
s!1
lim f ðtÞ ¼ lim sFðsÞ t!0
s!1
8 The Final Value Theorem This theorem refers to the behavior of the function f ðtÞ as t ! 1 and, for this reason, it is called the final value theorem. This theorem is given by the relation lim f ðtÞ ¼ lim sFðsÞ
t!1
ð2:3-17Þ
s!0
assuming that the Laplace transform of f ð1Þ ðtÞ exists and that the denominator of sFðsÞ has no roots on the imaginary axis or in the right-half complex plane. Proof Taking the limit of both sides of Eq. (2.3-6) as s ! 0, the left-hand side of this relation becomes ð1 ð1 ðt lim f ð1Þ ðtÞest dt ¼ f ð1Þ ðtÞ dt ¼ lim f ð1Þ ðÞ d s!0 0
0
t!1 0
¼ lim ½ f ðtÞ f ð0Þ ¼ lim f ðtÞ f ð0Þ t!1
t!1
while the right-hand side becomes lim½sFðsÞ f ð0Þ ¼ lim sFðsÞ f ð0Þ
s!0
s!0
Equating the resulting two sides, we readily have relation (2.3-17). Remark 2.3.2 Clearly, given FðsÞ, one can find the behavior of f ðtÞ as t ! 0 and as t ! 1, by first determining the inverse Laplace transform of FðsÞ, i.e., by determining f ðtÞ ¼ L1 FðsÞ and subsequently determining f ð0Þ and f ð1Þ using directly the function f ðtÞ. The initial and final value theorems greatly simplify this problem, since they circumvent the rather cumbersome task of determining L1 FðsÞ, and yield the values of f ð0Þ and f ð1Þ by directly applying the relations (2.3-16) and (2.3-17), respectively, which are simple to carry out.
38
Chapter 2
9 Multiplication of a Function by t The following relation holds Lftf ðtÞg ¼
d FðsÞ ds
ð2:3-18Þ
Proof Differentiating Eq. (2.3-2) with respect to s, we have ð1 d FðsÞ ¼ tf ðtÞest dt ¼ Lftf ðtÞg ds 0 Thus relation (2.3-18) is established. In the general case, the following relation holds
dn L tn f ðtÞ ¼ ð1Þn n FðsÞ ds
ð2:3-19Þ
10 Division of a Function by t The following relation holds: ð1 f ðtÞ L ¼ FðsÞ ds t s
ð2:3-20Þ
Proof Integrating Eq. (2.3-2) from s to 1, we have that ð1 ð1 ð1 ð1 ð1 FðsÞ ds ¼ f ðtÞet dt d ¼ f ðtÞet d dt s s 0 0 s ð1 ð1 ð f ðtÞ 1 st f ðtÞ st f ðtÞ e dt ¼ L de dt ¼ ¼ t s t t 0 0 In the general case, the following relation holds ð1 ð1 f ðtÞ FðÞðdÞn L ¼ tn s s |fflfflfflfflffl ffl{zfflfflfflfflfflffl}
ð2:3-21Þ
n times
11 Periodic Functions Let f ðtÞ be a periodic function with period T. Then, the Laplace transform of f ðtÞ is given by Lf f ðtÞg ¼
F1 ðsÞ ; 1 esT
F1 ðsÞ ¼ Lf f1 ðtÞg
ð2:3-22Þ
where f1 ðtÞ is the function f ðtÞ during the first period, i.e., for t 2 ½0; T: Proof The periodic function f ðtÞ can be expressed as a sum of time-delay functions as follows: f ðtÞ ¼ f1 ðtÞuðtÞ þ f1 ðt TÞuðt TÞ þ f1 ðt 2TÞuðt 2TÞ þ Taking the Laplace transform of f ðtÞ, we have
Mathematical Background
39
L f ðtÞg ¼ F1 ðsÞ þ F1 ðsÞest þ F1 ðsÞe2sT þ ¼ F1 ðsÞ½1 þ esT þ e2sT þ F1 ðsÞ ¼ 1 esT where use was made of the property (2.3-15). 2.4
THE INVERSE LAPLACE TRANSFORM
The inverse Laplace transform of a function FðsÞ is given by the relation (2.3-4). To avoid the calculation of the integral (2.3-4), which it is often quite difficult and time consuming, we usually use special tables (see Appendix A) which give the inverse Laplace transform directly. These tables cover only certain cases and therefore they cannot be used directly for the determination of the inverse Laplace transform of any function FðsÞ. The way to deal with cases which are not included in the tables is, whenever possible, to convert FðsÞ by using appropriate methods, in such a form that its inverse Laplace transform can be found directly in the tables. A popular such method is, when FðsÞ is a rational function of s (and this is usually the case), to expand FðsÞ in partial fractions, in which case the inverse Laplace transform is found directly in the tables. Therefore, our main interest here is to develop a method for expanding a rational function to partial fractions. To this end, consider the rational function FðsÞ ¼
bðsÞ bm sm þ bm1 sm1 þ þ b1 s þ b0 ¼ ; aðsÞ sn þ an1 sn1 þ þ a1 s þ a0
m
ð2:4-1Þ
Q Let 1 ; 2 ; . . . ; n be the roots of the polynomial aðsÞ, i.e., let aðsÞ ¼ ni¼1 ðs i Þ. We distinguish three cases: (a) all roots are real and distinct, (b) all roots are real but not all distinct, and (c) certain or all roots are complex conjugates. 1 Distinct Real Roots In this case the roots 1 ; 2 ; . . . ; n of the polynomial aðsÞ are real and distinct, i.e., 1 6¼ 2 6¼ 3 6¼ . . . 6¼ n . Here, FðsÞ can be expanded into a sum of n partial fractions, as follows: FðsÞ ¼
n X bðsÞ c1 ck cn ci ¼ þ þ þ þ ¼ aðsÞ s 1 s k s n s i i¼1
ð2:4-2Þ
The inverse Laplace transform of each term of FðsÞ can be found in the Laplace transform tables (Appendix A). From the tables, we have f ðtÞ ¼ L1 fFðsÞg ¼ c1 e1 t þ c2 e2 t þ þ cn en t The constants c1 ; c2 ; . . . ; cn , are determined as follows: multiply both sides of (2.4-2) by the factor s k to yield ðs k ÞFðsÞ ¼
s k s k s k c1 þ c2 þ þ ck þ þ c s 1 s 2 s n n
Taking the limit as s approaches the root k , we have ck ¼ lim ðs k ÞFðsÞ; s!k
k ¼ 1; 2; . . . ; n
ð2:4-3Þ
40
Chapter 2
Relation (2.4-3) is a very simple procedure for determining the constants c1 ; c2 ; . . . ; cn . 2 Nondistinct Real Roots In this case the roots 1 ; 2 ; . . . ; n of the polynomial aðsÞ are real but not distinct. For simplicity, let the polynomial aðsÞ have only one repeated root 1 , with multiplicity r, i.e., let aðsÞ ¼ ðs 1 Þr
n Y
ðs i Þ
ð2:4-4Þ
i¼rþ1
Then, FðsÞ is expanded into partial fractions, as follows: FðsÞ ¼
bðsÞ c1 c2 ck cr ¼ þ þ þ þ þ aðsÞ s 1 ðs 1 Þ2 ðs 1 Þr ðs 1 Þk crþ1 cn þ þ þ s rþ1 s n
Using the Laplace transform tables, the inverse Laplace transform of FðsÞ is ck tk1 e1 t þ f ðtÞ ¼ L1 fFðsÞg ¼ c1 e1 t þ c2 te1 t þ þ ðk 1Þ! cr tr1 e1 t þ crþ1 erþ1 t þ þ cn en t þ ðr 1Þ! The coefficients crþ1 ; crþ2 ; . . . ; cn are determined according to relation (2.4-3). To determine the coefficients ck , k ¼ 1; 2; . . . ; r, we work as follows: multiply both sides of Eq. (2.4-4) by the factor ðs 1 Þr to yield ðs 1 Þr FðsÞ ¼ ðs 1 Þr1 c1 þ þ ðs 1 Þrk ck þ þ cr þ
ðs 1 Þr ðs 1 Þr crþ1 þ þ c s rþ1 s n n
Differentiating by s, ðr kÞ times, both sides of the above relation and taking the limit as s ! 1 , all terms of the right-hand side go to zero, except for the term ðs 1 Þrk ck , which becomes ðr kÞ!ck . Hence 1 drk ½ðs 1 Þr FðsÞ; s!1 ðr kÞ! dsrk
ck ¼ lim
k ¼ 1; 2; . . . ; r
ð2:4-5Þ
Clearly, if more than one root is repeated, one may apply the above procedure for each repeated root. 3 Complex Roots Since all coefficients of aðsÞ are real numbers, it follows that all complex roots of aðsÞ appear in complex conjugate pairs. For simplicity, let the polynomial aðsÞ have only one such pair, say 1 ¼ þ j!, and 2 ¼ 1 ¼ j!, where the symbol ‘‘-‘‘ indicates the complex conjugate number. Here, FðsÞ may be expanded in partial fractions, as in the case of distinct real roots, as follows: c1 c2 c3 cn þ þ þ ð2:4-6Þ FðsÞ ¼ þ s 1 ðs 1 Þ s 3 s n
Mathematical Background
41
Using the tables, the inverse Laplace transform is
f ðtÞ ¼ L1 fFðsÞg ¼ c1 e1 t þ c2 e1 t þ
n X
c i e i t
i¼3
Using relation (2.4-3) we conclude that c2 ¼ c1 . If the complex roots are repeated, we use relation (2.4-5). Consequently, the results of cases (a) and (b) can be used in the present case, with the difference that the coefficients ci of complex roots appear always in conjugate pairs, a fact which simplifies their calculation. Consider the case where aðsÞ has the form aðsÞ ¼ ½ðs þ aÞ2 þ !2 qðsÞ, where qðsÞ has only real roots. This special form of aðsÞ is quite frequently met in practice. In this case and when the polynomial qðsÞ is of a low degree (e.g., first or second degree), the following partial fractions expansion of FðsÞ is recommended: FðsÞ ¼
bðsÞ bðsÞ 1 s þ 0 ¼ ¼ þ eðsÞ aðsÞ ½ðs þ aÞ2 þ !qðsÞ ðs þ aÞ2 þ !2
ð2:4-7Þ
where eðsÞ consists of all fractions which have as denominators the factors of qðsÞ. Consequently, eðsÞ can be determined using the results of this section. The coefficients 0 and 1 are determined from the following relation: 1 s þ 0 ¼ FðsÞ eðsÞ ðs þ aÞ2 þ !2 The motivation for presenting the method (2.4-7) is that it has computational advantages over relation (2.4-6), since the complex conjugate roots do not appear in determining 0 and 1 . Remark 2.4.1 In FðsÞ of relation (2.4-1), let m n, which means that the degree of the polynomial of the numerator is equal or greater than the degree of the polynomial of the denominator. In this case, the method of partial fractions expansion cannot be applied. To circumvent this difficulty, divide the numerator by the denominator to yield FðsÞ ¼
bðsÞ ðsÞ ¼ ðsÞ þ aðsÞ aðsÞ
ð2:4-8Þ
where ðsÞ is a polynomial of degree m n and ðsÞ is a polynomial of degree n 1. Clearly, the inverse Laplace transform of FðsÞ may now be found from the tables. 2.5
APPLICATIONS OF THE LAPLACE TRANSFORM
This section presents certain applications of the Laplace transform in the study of linear systems. Example 2.5.1 Determine the voltage across the capacitor of the circuit shown in Figure 2.11. The switch S closes when t ¼ 0. The initial condition for the voltage capacitor is zero, i.e. vc ð0Þ ¼ 0.
42
Chapter 2
Figure 2.11
RC network.
Solution From Kirchhoff’s voltage law we have ð 1 t RiðtÞ þ iðtÞ dt ¼ V0 C 1 Applying the Laplace transform to both sides of the integral equation, we get the following algebraic equation " # 1 IðsÞ ið1Þ ð0Þ V þ RIðsÞ þ ¼ 0 C s s s Ð 0 where IðsÞ ¼ LfiðtÞg and ið1Þ ð0Þ ¼ 1 iðtÞ dt ¼ Cvc ð0Þ ¼ 0. Replacing ið1Þ ð0Þ ¼ 0 in the above equation, we have 1 V V0 =R þR ¼ 0 IðsÞ or IðsÞ ¼ Cs s þ 1=RC s The inverse Laplace transform of IðsÞ is found in Appendix A and is as follows iðtÞ ¼ L1 fIðsÞg ¼
V0 t=RC e R
Hence, the voltage vc ðtÞ across the capacitor will be vc ðtÞ ¼ V0 RiðtÞ ¼ V0 V0 et=RC ¼ V0 ½1 et=RC Example 2.5.2 Consider the mechanical system shown in Figure 2.12, where y, K, m, and B are the position of the mass, the spring’s constant, the mass, and the friction coefficient, respectively. The initial conditions are yð0Þ ¼ 0 and yð1Þ ð0Þ ¼ 2. Let the applied force f ðtÞ ¼ uðtÞ; here uðtÞ is the unit step function. Determine the response yðtÞ of the mechanical system, where for simplicity, let m ¼ 1, B ¼ 3, and K ¼ 2. Solution Using d’Alemberts law of forces, the following differential equation is obtained: m
d2 y dy þ B þ Ky ¼ f ðtÞ dt dt2
Mathematical Background
Figure 2.12
43
A spring and a mass.
Using the particular values for m, B, K, and f ðtÞ, the differential equation becomes d2 y dy þ 3 þ 2y ¼ uðtÞ 2 dt dt Applying the Laplace transform to both sides of the differential equation, we arrive at the following algebraic equation s2 YðsÞ syð0Þ yð1Þ ð0Þ þ 3½sYðsÞ yð0Þ þ 2YðsÞ ¼
1 s
Solving for YðsÞ we have syð0Þ þ 3yð0Þ þ yð1Þ ð0Þ 1 þ 2 2 s þ 3s þ 2 sðs þ 3s þ 2Þ 2 1 ¼ 2 þ s þ 3s þ 2 sðs2 þ 3s þ 2Þ
YðsÞ ¼
where use was made of the given values of the initial conditions. Next, using the method of partial fraction expansion, we have YðsÞ ¼
2 1 2 1 þ þ 2 ¼ ðs þ 3s þ 2Þ sðs þ 3s þ 2Þ ðs þ 1Þðs þ 2Þ sðs þ 1Þðs þ 2Þ 2
or YðsÞ ¼
2 2 1=2 1 1=2 þ þ sþ1 sþ2 s sþ1 sþ2
Taking the inverse Laplace transform, yields that the position yðtÞ of the mass m is given by
yðtÞ ¼ 2et 2et þ 12 et þ 0:5e2t ¼ 12 et þ 32 e2t
44
Chapter 2
Example 2.5.3 Determine the voltage across the resistor R of the circuit shown in Figure 2.13. The capacitor C is initially charged at 10 V and the switch S closes when t ¼ 0. Solution From Kirchhoff’s voltage law we have ð di 1 t L þ Ri þ idt ¼ 0 dt C 1 Applying the Laplace transform to both sides of the integrodifferential equation, we get the following algebraic equation " # 1 IðsÞ ið1Þ ð0Þ þ L½sIðsÞ ið0Þ þ RIðsÞ þ ¼0 C s s or 1 ið1Þ ð0Þ IðsÞ ¼ Lið0Þ Ls þ R þ Cs Cs Replacing the values of the parameters of the circuit and the initial conditions vc ð0Þ ¼ ið1Þ ð0Þ=C ¼ 10 V and ið0Þ ¼ 0, we have 20 10 IðsÞ ¼ 5s þ 20 þ s s Solving this equation for IðsÞ, we get IðsÞ ¼
2 2 ¼ s þ 4s þ 4 ðs þ 2Þ2 2
Hence 1
1
iðtÞ ¼ L ½IðsÞ ¼ L
2 ¼ 2te2t ðs þ 2Þ2
Therefore, the voltage vR ðtÞ across the resistor will be vR ðtÞ ¼ 20iðtÞ ¼ 40te2t .
Figure 2.13
RLC network.
Mathematical Background
45
Figure 2.14 RC network with a pulse function as an input voltage. (a) Pulse of 1 sec duration; (b) RC network.
Example 2.5.4 Determine the voltage across the capacitor of the circuit shown in Figure 2.14. The input voltage is a pulse of 1 sec duration. The switch S closes when t ¼ 0. Solution From Kirchhoff’s voltage law we have ðt
106 iðtÞ þ 106
iðtÞ dt ¼ 10½uðtÞ uðt 1Þ
0
Applying the Laplace transform to the above equation, we get the following algebraic equation 105 IðsÞ þ
105 1 es IðsÞ ¼ s s s
Solving for IðsÞ, we get IðsÞ ¼ 10
5
1 es 1 es 5 ¼ 10 sþ1 sþ1 sþ1
Hence iðtÞ ¼ L1 fIðsÞg ¼ 105 ½et uðtÞ eðt1Þ uðt 1Þ where relation (2.3-15) was used. The voltage vc ðtÞ across the capacitor will be ðt
ðt h i et uðtÞ eðt1Þ uðt 1Þ dt 0 h0 i t ¼ 10 ½1 e uðtÞ ½1 eðt1Þ uðt 1Þ
vc ðtÞ ¼
1 C
iðtÞ dt ¼ 10
The plot of the capacitor voltage is given in Figure 2.15.
46
Chapter 2
Figure 2.15
2.6
The voltage capacitor of Example 2.5.4.
MATRIX DEFINITIONS AND OPERATIONS
2.6.1
Matrix Definitions
Consider the system of linear algebraic equations a11 x1 þ a12 x2 þ a1n xn ¼ b1 a21 x1 þ a22 x2 þ þ a2n xn .. .. .. . . .
¼ b2 .. .
ð2:6-1Þ
an1 x1 þ an2 x2 þ þ ann xn ¼ bn This system can be written compactly as: Ax ¼ b where
2
ð2:6-2Þ
a11 6 a21 6 A ¼ 6 .. 4 .
a12 a22 .. .
an1
an2
3 a1n a2n 7 7 .. 7; . 5 ann
3 x1 6 x2 7 6 7 x ¼ 6 .. 7; 4 . 5 2
xn
3 b1 6 b2 7 6 7 and b ¼ 6 .. 7 4 . 5 2
bn
The rectangular array, which has n columns and n rows, designated by the upper case letter A, is called a matrix. Matrices which have only one column, designated by lower case letters (i.e., x and b) are called vectors and have n 1 dimensions. A can also be written as A ¼ ½aij , i; j ¼ 1; 2; . . . ; n. The parameters aij are called the elements of matrix A. One of the basic reasons which lead to the use of matrices is that they provide a concise description of multivariable systems. For example, relation (2.6-2) is a concise description of the algebraic equations (2.6-1). Furthermore, investigation of the solution of Eq. (2.6-1) is simplified when using Eq. (2.6-2). For example, we say that Eq. (2.6-2) has a unique solution if the determinant (see Section 2.6-2) of A is nonzero. We present below certain useful types of matrices.
Mathematical Background
47
1 The Column Vector This matrix is composed of a single column, i.e., it has the form 2 3 a1 6 a2 7 6 7 a ¼ 6 .. 7 4 . 5
ð2:6-3Þ
an 2 The Row Vector This matrix is composed of a single row, i.e., it has the form aT ¼ ½a1
a2
an
ð2:6-4Þ
3 The Square and Nonsquare Matrices A square matrix is one which has an equal number of rows and columns, whereas a nonsquare matrix has an unequal number of rows and columns. 4 The Diagonal Matrix The diagonal matrix is a square matrix whose elements are all zero, except those that lie on the main diagonal and has the form 2 3 a11 0 0 6 0 a22 0 7 7 6 A ¼ 6 .. ð2:6-5Þ .. .. 7 ¼ diagfa11 ; a22 ; . . . ; ann g 4 . . . 5 0 0 ann 5 The Identity Matrix A diagonal matrix that has ones along the main diagonal and zeros elsewhere is called an ðn nÞ identity matrix. This matrix is denoted by I and has the form 3 2 1 0 0 60 1 07 ð2:6-6Þ I¼6 .. 7 4 ... ... .5 0
0
1
6 The Zero Matrix This is a matrix whose elements are all zero. 7 The Singular and Nonsingular Matrices If the determinant of a square matrix is zero, the matrix is called singular, while if the determinant is nonzero, the matrix is called nonsingular. 8 The Transpose of a Matrix The matrix AT is the transpose of A if the rows of the first matrix are the columns of the second. Hence, if A ¼ ½aij , then AT ¼ ½aji . Therefore, if A has dimensions n m, then AT has dimensions m n. The superscript T denotes transposition.
48
Chapter 2
9 The Symmetric Matrix The symmetric matrix is a square matrix for which A ¼ AT or aij ¼ aji ; 8i; j. 10 The Triangular Matrix The triangular matrix is a square matrix which has one of the following two forms 3 3 2 2 a11 0 0 a11 a12 a1n 6 0 a22 a2n 7 6 a21 a22 0 7 7 7 6 6 A ¼ 6 .. or B ¼ 6 .. ð2:6-7Þ .. 7 .. 7 .. .. 5 4 . 4 . 5 . . . . an1 an2 ann 0 0 ann Matrix A is called upper triangular, whereas matrix B is called lower triangular. 11 The Conjugate Matrix is called the conjugate matrix of A and its elements are the conjugate The matrix A ¼ ½a ij . elements of A: that is, if A ¼ ½aij , then A 12 The Hermitian Matrix T , then the matrix A is called Hermitian. If A ¼ A 13 The Orthogonal Matrix A matrix A is called orthogonal if it is a square matrix with real elements and the following relation holds: AT A ¼ AAT ¼ I
ð2:6-8Þ
Other useful definitions regarding matrices are: a. The Trace The trace of an n n square matrix A ¼ ½aij is denoted as trA or trace A and is defined as the sum of all the elements of the main diagonal, i.e., trA ¼
n X
aii
ð2:6-9Þ
i¼1
b. The Rank The rank of a matrix is equal to the dimension of its largest non-singular (square) submatrix. 2.6.2
Matrix Operations
1 Matrix Addition Consider the matrices A ¼ ½aij and B ¼ ½bij , both of whose dimensions are n m. Then, their addition A þ B is the n m matrix C ¼ ½cij , whose elements cij are cij ¼ aij þ bij .
Mathematical Background
49
2 Matrix Multiplication Consider the matrices A ¼ ½aij and B ¼ ½bij of dimensions n m and m p, respectively. Then, their product AB is the n p matrix C ¼ ½cij , whose elements cij are given by cij ¼
m X
aik bkj ¼ aTi bj
k¼1
where aTi is the ith row of matrix A and bj is the jth column of matrix B. Hence, every cij element is determined by multiplying the ith row of the matrix A with the jth column of the matrix B. 3 Multiplying a Matrix with a Constant Consider the matrix A ¼ ½aij and the constant k. Then, every element of the matrix C ¼ kA is simply cij ¼ kaij . 4 Transpose of a Matrix Product Consider the matrix product A1 A2 . . . Am . Then ðA1 A2 . . . Am ÞT ¼ ATm ATm1 . . . AT1
ð2:6-10Þ
5 Derivatives of a Matrix Consider the matrix A ¼ ½aij , whose elements aij are functions of time t, and the function f which is a scalar function of time. Then, the following relations hold: daij dA ¼ ð2:6-11Þ dt dt dð f AÞ df dA ¼ Aþf dt dt dt dðA þ BÞ dA dB ¼ þ dt dt dt dðABÞ dA dB ¼ BþA dt dt dt dðA1 Þ dA 1 ¼ A1 A dt dt
6 Derivatives of a Matrix with Respect to a Vector The following relations hold
ð2:6-12Þ
ð2:6-13Þ ð2:6-14Þ
ð2:6-15Þ
50
Chapter 2
3 @f 6 @x1 7 7 6 6 @f 7 7 6 7 6 @f @x2 7 ¼6 6 @x 6 . 7 7 6 .. 7 7 6 4 @f 5 @xn 2
2
and
@y1 6 @x1 6 6 @y 6 1 T 6 @y @x ¼6 6 2 @x 6 .. 6 . 6 4 @y1 @xn
@y2 @x1 @y2 @x2 .. . @y2 @xn
3 @ym @x1 7 7 @ym 7 7 7 @x2 7 7 .. 7 . 7 7 @ym 5 @xn
@½QðtÞyðtÞ ¼ QT ðtÞ @y
ð2:6-16Þ
ð2:6-17Þ
1 @½yT ðtÞQðtÞyðtÞ ¼ QðtÞyðtÞ 2 @y " # 1 @½yT ðtÞQðtÞyðtÞ @yT ðtÞ ¼ QðtÞyðtÞ 2 @x @x
ð2:6-18Þ ð2:6-19Þ
where yT ¼ ð y1 ; y2 ; . . . ; ym Þ and QðtÞ is a symmetric matrix with dimensions m m. 7 Matrix Integration Here
ð
ð A dt ¼
2.7
aij dt
ð2:6-20Þ
DETERMINANT OF A MATRIX
1 Calculation of the Determinant of a Matrix The determinant of an n n matrix A is denoted by jAj or det A, and it is scalar quantity. A popular method to calculate the determinant of a matrix is the Laplace expansion in which the determinant of an n n matrix A ¼ ½aij is the sum of the elements of a row or a column, where each element is multiplied by the determinant of an appropriate matrix, i.e., jAj ¼
n X
aij cij ;
j ¼ 1 or 2 or or n (column expansion)
ð2:7-1Þ
aij cij ;
i ¼ 1 or 2 or or n (row expansion)
ð2:7-2Þ
i¼1
or jAj ¼
n X j¼1
where cij is defined as follows: cij ¼ ð1Þiþj jMij j where Mij is the ðn 1Þ ðn 1Þ square matrix formed from A by deleting the ith row and the jth column from the original matrix.
Mathematical Background
51
2 The Determinant of a Matrix Product Consider the square matrices A1 ; A2 ; A3 ; . . . ; Am and their product B ¼ A1 A2 . . . Am . Then, jBj ¼ jA1 jjA2 j jAm j
2.8
ð2:7-3Þ
THE INVERSE OF A MATRIX
1 Calculation of the Inversion of a Matrix The inverse of an n n square matrix A is denoted by A1 and has the following property A1 A ¼ AA1 ¼ I 1
The inverse matrix A A1 ¼
ð2:8-1Þ is determined as follows:
Adj A jAj
ð2:8-2Þ
where the matrix adjA, called the adjoint matrix of A, is the matrix whose elements are the adjoint elements of A. 2 The Inverse of a Matrix Product Consider the n n square B ¼ A1 A2 Am . Then
matrices
A1 ; A2 ; . . . ; Am
and
their
product
1 1 B1 ¼ A1 m Am1 A1
2.9
MATRIX EIGENVALUES AND EIGENVECTORS
The eigenvalues of a matrix are of significance in the study of control systems, since their behavior is strongly influenced by their eigenvalues. The issue of the eigenvalues of an n n square matrix A ¼ ½aij stems from the following problem: consider the ndimensional vectors uT ¼ ðu1 ; u2 ; . . . ; un Þ and yT ¼ ðy1 ; y2 ; . . . ; yn Þ and the relation y ¼ Au
ð2:9-1Þ
The foregoing relation is a transformation of the vector u onto the vector y through the matrix A. One may ask the following question: are there nonzero vectors u which maintain their direction after such transformation? If there exists such a vector u, then the vector y is proportional to the vector u, i.e., the following holds: y ¼ Au ¼ u
ð2:9-2Þ
where is a constant. From relation (2.9-2) we get that Au ¼ u or Au u ¼ 0, and if we set u ¼ Iu, then we have ðI AÞu ¼ 0
ð2:9-3Þ
The system of equations (2.9-3) has a nonzero solution if the determinant of the matrix I A is equal to zero, i.e., if jI Aj ¼ 0
ð2:9-4Þ
52
Chapter 2
Equation (2.9-4) is a polynomial equation of degree n and has the general form jI Aj ¼ pðÞ ¼ n þ a1 n1 þ a2 n2 þ þ an1 þ an n Y ¼ ð i Þ ¼ 0
ð2:9-5Þ
i¼1
The roots 1 ; 2 ; . . . ; n of Eq. (2.9-5) are called the eigenvalues of the matrix A. These eigenvalues, if set in Eq. (2.9-3), produce nonzero solutions to the problem of determining the vectors u, which maintain their direction after the transformation Au. The polynomial pðÞ is called the characteristic polynomial of the matrix A and Eq. (2.9-5) is called the characteristic equation of the matrix A. A vector ui is an eigenvector of matrix A and corresponds to the eigenvalue i if ui 6¼ 0 and relation (2.9-2) is satisfied, i.e., Aui ¼ i ui
ð2:9-6Þ
In determining the characteristic polynomial pðÞ of A, considerable computational effort may be required in computing the coefficients a1 ; a2 ; . . . ; an of pðÞ from the elements of matrix A, particularly as n becomes large. Several numerical methods have been proposed, one of the most popular of which is Bocher’s recursive relation: 1 ak ¼ ðak1 T1 þ ak2 T2 þ þ a1 Tk1 þ Tk Þ; k
k ¼ 1; 2; . . . ; n
ð2:9-7Þ
where Tk ¼ trAk , k ¼ 1; 2; . . . ; n. The description of this method is simple but has a serious numerical drawback in that it requries the computation up to the nth power of the matrix A. Certain interesting properties of matrix A, of its eigenvalues, and of the coefficients of pðÞ, which may immediately be derived from the relations (2.9-5) to (2.9-7), are the following: 1.
If the matrix A is singular, then it has at least one zero eigenvalue and vice versa.
2.
trA ¼ 1 þ 2 þ þ n
(2.9-8)
3.
jAj ¼ 1 2 . . . n
(2.9-9)
4.
an ¼ ð1Þ jAj ¼ ð1Þ 1 2 . . . n ¼ pð0Þ n
n
(2.9-10)
Example 2.9.1 Find the characteristic polynomial, the eigenvalues, and the eigenvectors of the matrix 1 1 A¼ 0 2 Solution The characteristic polynomial pðÞ of the matrix A, accoding to definition (2.9-5), is þ 1 1 pðÞ ¼ jI Aj ¼ det ¼ ð þ 1Þð þ 2Þ ¼ 2 þ 3 þ 2 0 þ2
Mathematical Background
53
The coefficients of pðÞ may be determined from the recursive equation (2.9-7). In this case, we first compute T1 and T2 . We have: T1 ¼ trA ¼ 3 and T2 ¼ trA2 ¼ 5. Hence a1 ¼ T1 ¼ 3
and
a2 ¼ 12 ða1 T1 þ T2 Þ ¼ 12 ð9 þ 5Þ ¼ 2
Therefore, pðÞ ¼ 2 þ a1 þ a2 ¼ 2 þ 3 þ 2. We observe that, working in two different ways, we arrive at the same characteristic polynomial, as expected. The eigenvalues of matrix A are the roots of the characteristic polynomial pðÞ ¼ 2 þ 3 þ 2 ¼ ð þ 1ð þ 2Þ, and hence we immediately have that the eigenvalues of matrix A are 1 ¼ 1 and 2 ¼ 2. The eigenvectors can be determined from relation (2.9-6). Let the vector u1 , which corresponds to the eigenvalue 1 ¼ 1, have the form u1 ¼ ð!1 ; !2 ÞT . Then, relation (2.9-6) becomes 1 1 !1 ! 0 1 !1 0 ¼ 1 or ¼ 0 2 !2 !2 0 1 !2 0 Thus, the eigenvector u1 is u1 ¼ ð!1 ; !2 ÞT ¼ ðd; 0ÞT , 8d 2 R, where R is the space of real numbers. In the same way, we calculate the eigenvector u2 ¼ ðv1 ; v2 ÞT , which corresponds to the eigenvalue 2 ¼ 2. From (2.9-6) we have 1 1 v1 v1 1 1 v1 0 ¼ 2 or ¼ 0 2 v2 v2 0 0 v2 0 Hence, the eigenvector u2 is u2 ¼ ðv1 ; v2 ÞT ¼ ðk; kÞT , 8k 2 R. Setting d ¼ k ¼ 1, we have the following two eigenvectors 1 1 u1 ¼ ; u2 ¼ 0 1
2.10
SIMILARITY TRANSFORMATIONS
Consider the n n square matrices A and B. The matrix B is similar to the matrix A if there exists an n n nonsingular matrix T such that: B ¼ T1 AT
ð2:10-1Þ
It is obvious that if relation (2.10-1) is true, then it is also true that A ¼ TBT1 Hence, if B is similar to A, then A must be similar to B. Relation (2.10-1) is called a similarity transformation. The similarity transformation usually aims at simplifying the matrix A. Such a simplification facilitates the solution of certain problems. For example, consider the linear system of first-order differential equations x_ ¼ Ax;
ð2:10-2Þ
xð0Þ
where xT ¼ ðx1 ; x2 ; . . . ; xn Þ. The vector xð0Þ is the initial vector of the system. Setting x ¼ Tz, where zT ¼ ðz1 ; z2 ; . . . ; zn Þ is a new vector, the system (2.10-2) becomes z_ ¼ T1 ATz;
zð0Þ ¼ T1 xð0Þ
ð2:10-3Þ
54
Chapter 2
If the similarity transformation matrix T is chosen so that T1 AT ¼ , ¼ diagð1 ; 2 ; . . . ; n Þ, where 1 ; 2 ; . . . ; n are the eigenvalues of matrix A, then Eq. (2.10-3) becomes z_ ¼ ,z
z_i ¼ i zi ;
or
i ¼ 1; 2; . . . ; n
ð2:10-4Þ
Comparing relations (2.10-2) and (2.10-4), we realize that the similarity transformation has simplified (decoupled) the vector differential system of equations (2.10-2) into n first-order scalar differential equations whose ith solution is given by the simple relation zi ðtÞ ¼ zi ð0Þ ei t In using the similarity transformation (2.10-1), one has to determine the transformation matrix T. In the case where the similarity transformation aims at diagonalizing the matrix A, then the determination of T is directly linked to the problem of eigenvalues and eigenvectors of A. In this case, the following holds true: 1. If the eigenvalues 1 ; 2 ; . . . ; n of the matrix A are distinct, then the eigenvectors u1 ; u2 ; . . . ; un are linearly independent. In this case, the similarity transformation matrix T which diagonalizes A, is denoted by M and has the form . . . M ¼ ½u1 .. u2 .. ..un
ð2:10-5Þ
The matrix M is called the eigenvector matrix. 2. If the eigenvalues 1 ; 2 ; . . . ; n of the matrix A are not distinct, then the eigenvectors u1 ; u2 ; . . . ; un are not always linearly independent. Since the diagonalization of A has as a necessary and sufficient condition the existence of n linearly independent eigenvectors of the matrix A, it follows that in the case where the eigenvalues of A are not distinct, the matrix A can be diagonalized only if it has n linearly independent eigenvectors. When A does not have n linearly independent eigenvectors, the matrix cannot be transformed into a diagonal form. However, it can be transformed to an ‘‘almost diagonal’’ form, known as the Jordan canonical form and is denoted by J. The general form of the matrix J is the following: 3 2 J11 ð1 Þ 7 6 0 J21 ð1 Þ 7 6 .. 7 6 7 6 . 7 6 7 6 Jk1 ð1 Þ ð2:10-6Þ J¼6 7 7 6 ð Þ J 12 2 7 6 .. 7 6 5 4 0 . Jmp ðp Þ where
2
i 60 6 6 . Jji ði Þ ¼ 6 .. 6 40 0
1 i .. .
0 1 .. .
0 0
0 0
0 0 .. .
i 0
0 0 .. .
3
7 7 7 7 7 15 i
where Jji ði Þ is called the Jordan submatrix.
Mathematical Background
55
Certain useful properties of the similarity transformation are that the characteristic polynomial (and therefore the eigenvalues), the trace, and the determinant of a matrix are invariable under the similarity transformation. However, it should be noted that, in general, the reverse does not hold true. If, for example, two matrices have the same characteristic polynomial, that does not necessarily mean that they are similar. Moreover, if B ¼ T1 AT and u is an eigenvector of A, then the vector T1 u is an eigenvector of B. Example 2.10.1 Diagonalize the matrix A of Example 2.9.1. Solution From Example 2.9.1 we have the two eigenvalues u1 ¼ ð1; 0ÞT and u2 ¼ ð1; 1ÞT . According to the relation (2.10-5), the similarity matrix M is .. 1 1 1 1 1 and M ¼ M ¼ ½u1 . u2 ¼ 0 1 0 1 Since u1 and u2 are linearly independent, the matrix M diagonalized the matrix A. Indeed 1 1 1 1 1 1 1 0 1 ¼ , ¼ M AM ¼ 0 1 0 2 0 1 0 2
2.11
THE CAYLEY–HAMILTON THEOREM
The Cayley–Hamilton theorem relates a matrix to its characteristic polynomial, as follows: Theorem 2.11.1 Consider an nn matrix A with characteristic polynomial pðÞ ¼ jI Aj ¼ n þ a1 n1 þ þ an1 þ an . Then, the matrix A satisfies its characteristic polynomial, i.e. pðAÞ ¼ An þ a1 An1 þ þ an1 A þ an I ¼ 0
ð2:11-1Þ
The Cayley–Hamilton theorem has several interesting and useful applications. Some of them are given below. a. Calculation of the Inverse of a Matrix From relation (2.11-1), we have an I ¼ ½An þ a1 An1 þ þ an1 A Let the matrix A be nonsingular. Then, multiplying both sides of the foregoing relation with A1 , we obtain an A1 ¼ ½An1 þ a1 An2 þ þ an1 I or
56
Chapter 2
A1 ¼
1 n1 ½A þ a1 An2 þ þ an1 I an
ð2:11-2Þ
where an ¼ ð1Þn jAj 6¼ 0. Relation (2.11-2) expresses the matrix A1 as a matrix polynomial of A of degree n 1 and presents an alternate way of determining A1 , different from that of Section 2.8, which essentially requires the calculation of the matrices Ak , k ¼ 2; 3; . . . ; n 1. Example 2.11.1 Consider the matrix 0 1 A¼ 6 5 Show that the matrix A satisfies its characteristic equation and calculate A1 . Solution We have pðÞ ¼ jI Aj ¼ 2 þ 5 þ 6 whence pðAÞ ¼ A2 þ 5A þ 6I 6 5 0 ¼ þ5 30 19 6
1 1 þ6 5 0
0 0 0 ¼ 1 0 0
Therefore pðAÞ ¼ 0. For the calculation of A1 , using relation (2.11-2), we have 1 1 5 1 1 A ¼ ðA þ 5IÞ ¼ 6 6 6 0 b. Calculation of Ak From relation (2.11-1) we have An ¼ ½a1 An1 þ þ an1 A þ an I
ð2:11-3Þ
Multiplying both sides of the above equation with the matrix A we have Anþ1 ¼ ½a1 An þ þ an1 A2 þ an A
ð2:11-4Þ
Substituting Eq. (2.11-3) into Eq. (2.11-4) we have Anþ1 ¼ ½a1 ða1 An1 þ þ an1 A þ an IÞ þ þ an1 A2 þ an A
ð2:11-5Þ
Relation (2.11-5) expresses the matrix Anþ1 as a linear combination of the matrices I, A; . . . ; An1 . The general case is Ak ¼
n1 X ð k Þi Ai ;
kn
ð2:11-6Þ
i¼0
where ð k Þi are constants depending on the coefficients a1 ; a2 ; . . . ; an of the characteristic polynomial pðÞ.
Mathematical Background
57
Example 2.11.2 Calculate the matrix A5 , where 0 1 A¼ 2 1 Solution From the Cayley–Hamilton theorem we have pðAÞ ¼ A2 A 2I ¼ 0 or A2 ¼ A þ 2I. From relation (2.11-5) we get A3 ¼ A2 þ 2A ¼ ðA þ 2IÞ þ 2A ¼ 3A þ 2I, A4 ¼ 3A2 þ 2A ¼ 3ðA þ 2IÞ þ 2A ¼ 5A þ 6I and A5 ¼ 5A2 þ 6A ¼ 5ðA þ 2IÞþ 6A ¼ 11A þ 10I. Therefore 0 1 1 0 10 11 A5 ¼ 11A þ 10I ¼ 11 þ 10 ¼ 2 1 0 1 22 21
2.12
QUADRATIC FORMS AND SYLVESTER THEOREMS
Consider a second-order polynomial of the form gðxÞ ¼
n X n X
ð2:12-1Þ
aij xi xj
j¼1 i¼1
where xT ¼ ðx1 ; x2 ; . . . ; xn Þ is a vector of real parameters. The polynomial gðxÞ is called the quadratic form of n variables. Some of the properties and definitions of gðxÞ are the following: 1.
The polynomial gðxÞ may be written as gðxÞ ¼ xT Ax;
2.
4. 5. 6. 7.
i; j ¼ 1; 2; . . . ; n
ð2:12-2Þ
where A is an n n real symmetric matrix (i.e., A ¼ AT Þ. The polynomial gðxÞ may also be written as an inner product as follows: gðxÞ ¼ hx; Axi
3.
A ¼ ½aij ;
ð2:12-3Þ
where ha; bi ¼ aT b, and a and b are vectors of the same dimension. The matrix A is called the matrix of the quadratic form gðxÞ. The rank of the matrix A is the order of gðxÞ. The polynomial gðxÞ is positive (negative) if its value is positive (negative) or zero for every set of real values of x. The polynomial gðxÞ is positive definite (negative definite) if its value is positive (negative) for all x and equal to zero only when x ¼ 0. The polynomial gðxÞ is positive semidefinite (negative semidefinite) if its value is positive (negative) or zero. The polynomial gðxÞ is undefined if its value can take positive as well as negative values.
A central problem referring to quadratic forms pertains to the determination of whether or not a quadratic form is positive, negative definite, or semidefinite. Certain theorems due to Sylvester, are presented below:
58
Chapter 2
Theorem 2.12.1 Let rankA ¼ m. Then, the polynomial gðxÞ ¼ xT Ax may be written in the following form pðyÞ ¼ y21 þ þ y2 y2 þ1 y2 þ þ 0y2 þ þ1 þ þ 0y2n
ð2:12-4Þ
where x ¼ Ty. The intergers and correspond to the number of positive and negative eigenvalues of A and are such that þ ¼ m. Theorem 2.12.2 From Eq. (2.12-4) we obtain pðyÞ and, hence, the polynomial gðxÞ is: 1. 2. 3. 4. 5.
Positive definite if ¼ n and ¼ 0 Positive semidefinite if < n and ¼ 0 Negative definite if ¼ 0 and ¼ n Negative semidefinite if ¼ 0 and < n Undefined if 6¼ 0
Theorem 2.12.3 The necessary and sufficient conditions for gðxÞ to be positive definite are i > 0;
i ¼ 1; 2; . . . ; n
where a 1 ¼ a11 ; 2 ¼ 11 a21
ð2:12-5Þ
a11 a21 .. ; . . . ; ¼ . n a22 an1
an1 .. . ann
Theorem 2.12.4 The necessary and sufficient conditions for gðxÞ to be negative definite are >0 when i is even i ð2:12-6Þ <0 when i is odd for i ¼ 1; 2; . . . ; n. Theorem 2.12.5 The necessary and sufficient conditions for gðxÞ to be negative semidefinite are i 0;
i ¼ 1; 2; . . . ; n 1
and
n ¼ 0
ð2:12-7Þ
Theorem 2.12.6 The necessary and sufficient conditions for gðxÞ to be negative semidefinite are 1 0; 2 0; 3 0; . . .
and
n ¼ 0
ð2:12-8Þ
We note that Theorem 2.12.2 has the drawback, over Theorems 2.12.3–2.12.6, in that it requires the calculation of the integers and of the eigenvalues and of the rank m of matrix A. For this reason, Theorems 2.12.3–2.12.6 are used more often.
Mathematical Background
59
Example 2.12.1 Find if the quadratic forms g1 ðxÞ ¼ 4x21 þ x22 þ 2x23 þ 2x1 x2 þ 2x1 x3 þ 2x2 x3 g2 ðxÞ ¼ 2x21 4x22 x23 þ 2x1 x2 þ 2x1 x3 g3 ðxÞ ¼ 4x21 x22 12x23 þ 2x1 x2 þ 6x2 x3 are positive or negative definite. Solution We have g1 ðxÞ ¼ xT Ax; and
2
3 x1 x ¼ 4 x2 5; x3
4 1 ¼ 4 > 0; 2 ¼ 1
1 ¼ 3 > 0; 1
2
4 A4 1 1
3 1 1 1 15 1 2
3 ¼ jAj ¼ 3 > 0
Hence, according to Theorem 2.12.3, g1 ðxÞ is positive definite. Also 2 3 2 3 2 1 1 x1 A ¼ 4 1 4 05 x ¼ 4 x2 5; g2 ðxÞ ¼ xT AX; 1 0 1 x3 and
2 1 ¼ 2 < 0; 2 ¼ 1
1 ¼ 7 > 0; 3 ¼j A j¼ 3 < 0 4
Hence, according to Theorem 2.12.4, g2 ðxÞ is negative definite. Finally, 2 3 2 3 4 1 0 x1 T A ¼ 4 1 1 35 x ¼ 4 x2 5; g3 ðxÞ ¼ x Ax; 0 3 12 x3 and
4 1 ¼ 4 < 0; 2 ¼ 1
1 ¼ 3 > 0; 3 ¼ jAj ¼ 0 1
Hence, according to Theorem 2.12.6, g3 ðxÞ is negative semidefinite. 2.13
PROBLEMS
1. Find the Laplace transform of the following functions: (a) (b) (c) (d) (e) (f)
t3 eat sinð!t þ Þ t cosð!t þ Þ Etuðt TÞ eT 2 uðt TÞ ðt 2Þuðt 2Þ
(g) (h) (i) (j) (k) (l)
ðt þ T1 Þeat uðt T2 Þ eðt1Þ uðt 1Þ ðt þ T1 Þuðt T1 Þ ðt þ T2 Þ2 uðt T2 Þ t cos at tet cos 2t
(m) (n) (o) (p) (q) (r)
cos !ðt TÞ t2 sin t 5 sin½!ðt Þuðt Þ sinðt TÞuðt TÞ sin t uðt TÞ sinðt TÞuðtÞ
60
Chapter 2
2. Find the Laplace transform of the periodic waveforms shown in Figure 2.16. 3. Obtain the initial values f ð0þ Þ and f ð1Þ ð0þ Þ, given that FðsÞ ¼
3 s þ 0:2
4. Obtain the final values of the functions having the following Laplace transforms: (a) Lf f ðtÞg ¼ FðsÞ ¼
Figure 2.16
3 sðs þ s þ 2Þ 2
Mathematical Background
(b) Lf f ðtÞg ¼ FðsÞ ¼
61
sþ2 sðs2 þ 4Þ
5. Find the inverse Laplace transforms of the functions (a)
1 ðs þ 1Þðs þ 2Þðs þ 3Þ
(b)
1 ðs þ aÞðs þ bÞ2
(c)
1 s 2s þ 9
(d)
sþ4 s2 þ 4s þ 8
(e)
1 sðs2 þ 4Þ
(f)
sþ1 sðs2 þ 4Þ
(g)
sþ3 ðs 2Þðs þ 1Þ
(h)
8 s3 ðs2 s 2Þ
(i)
1 ðs2 þ 1Þðs2 þ 4s þ 8Þ
(j)
s2 þ 1 s2 þ 2
(k)
es s2 þ 1
(l)
eTs s 4 þ a4
(m)
eTs s
(n)
1 es s2
2
6. Find the current iðtÞ and the voltage vL ðtÞ across the inductor for the RL circuit shown in Figure 2.17. The switch S is closed at t ¼ 0 and the initial condition for the inductor current is zero ðiL ð0Þ ¼ 0Þ: 7. Find the current iðtÞ for the RC circuit shown in Figure 2.18. The switch S is closed at t ¼ 0 and the initial condition for the capacitor voltage is zero ðvc ð0Þ ¼ 0Þ. 8. Find the current iðtÞ for the LC circuit shown in Figure 2.19. The switch S is closed at t ¼ 0 and the initial conditions for the inductor and the capacitor are iL ð0Þ ¼ 0 and vc ð0Þ ¼ 0. 9. For the RLC circuit shown in Figure 2.20, find the voltage across the resistor R using the Laplace transform method. Assume that the switch S is closed at t ¼ 0. 10. Using the Laplace transform method, find the currents of the circuits shown in Figure 2.21. 11. Obtain the output voltage vR ðtÞ of the highpass filter shown in Figure 2.22.
Figure 2.17
62
Chapter 2
Figure 2.18
Figure 2.19
Figure 2.20 12. Find the voltage across the resistor R ¼ 10 for the circuit shown in Figure 2.23. Initially, the capacitor is fully charged and the switch S is moved from position a to position b. 13. Consider the frictionless movement of a mass m shown in Figure 2.24, where f ðtÞ is the force applied, yðtÞ is the displacement of the mass, and m ¼ 1 kg. Using the Laplace transform, find yðtÞ for t > 0 if f ðtÞ has the form shown in Figure 2.24 and the initial conditions are yð0Þ ¼ 1 and y_ ð0Þ ¼ 0. 14. Solve Problem 13 for the system shown in Figure 2.25, where K is the spring constant. Consider the case where K ¼ 0:5.
Mathematical Background
Figure 2.21
Figure 2.22
Figure 2.23
63
64
Chapter 2
Figure 2.24
15. Determine the eigenvalues and the eigenvectors and diagonalize the following matrices: A¼
2 4
0 ; 3
2
3 0 6 D ¼ 40 3 2 2
B¼ 2
0 2
2
1 ; 3
0 6 C ¼ 40
0 2
3
3 0 7 0 5;
1 0
3
7 2 5 1
16. Solve the system of differential equations x_ ¼ Ax;
xð0Þ ¼ x0
where 2
3 x1 x ¼ 4 x2 5; x3
2
0 A¼4 0 6
1 0 11
3 0 1 5; 6
3 1 x0 ¼ 4 1 5 2
using the diagonalization method for the matrix A.
Figure 2.25
2
Mathematical Background
65
17. For the following matrices 2
1 A ¼ 41 0
3 1 0 3 0 5; 0 2
1 B¼ 2
2 ; 3
2
1 C ¼ 41 0
2 3 6
3 2 15 3
show that they satisfy their characteristic equations and find A1 , B1 , and C1 . Also obtain B3 and C8 . 18. Determine whether the following quadratic forms are positive or negative definite. g1 ðxÞ ¼ x21 þ 2x1 x2 þ 4x22 g2 ðxÞ ¼ x21 2x1 x2 2x22 g3 ðxÞ ¼ ðx1 x2 Þ2 g4 ðxÞ ¼ ðx1 x2 Þ2 g5 ðxÞ ¼ x21 x22
g6 ðxÞ ¼ 10x21 þ x22 þ x23 þ 2x1 x2 þ 8x1 x3 þ x2 x3 g7 ðxÞ ¼ x21 2x22 þ 2x23 þ 2x1 x2 g8 ðxÞ ¼ x21 2x22 þ 2x23 g9 ðxÞ ¼ 6x21 þ 4x22 þ 2x23 þ 4x1 x2 4x2 x3 2x1 x3 g10 ðxÞ ¼ 2x21 5x22 10x23 þ 4x1 x2 2x1 x3 6x2 x3
BIBLIOGRAPHY Books 1. 2. 3.
JJ D’Azzo, CH Houpis. Linear Control System Analysis and Design, Conventional and Modern. New York: McGraw-Hill, 1975. BC Kuo. Automatic Control Systems. London: Prentice Hall, 1995. K Ogata. Modern Control Systems. London: Prentice Hall, 1997.
3 Mathematical Models of Systems
3.1
INTRODUCTION
The general term system description, loosely speaking, refers to a mathematical expression that appropriately relates the physical system quantities to the system components. This mathematical relation constitutes the mathematical model of the system. As an example, consider a resistor whose voltage v, current i, and resistance R are related in the well-known equation v ¼ Ri. This expression relates the physical quantities (variables) v and i with the component R and constitutes a description; i.e., it constitutes a mathematical model of this simple (one-component) system. As another example, consider a circuit composed of a resistor and an inductor connected in series. In this case, the voltage v across the circuit, the current i, the resistance R of the resistor, and the self-inductance L of the inductor are related with the well-known relation v ¼ Lðdi=dtÞ þ Ri. This expression relates the physical quantities v and i with the components R and L and constitutes a mathematical model of the system. As already mentioned in Chap. 1, a system in operation involves the following three elements: the system’s input (or excitation), the system itself, and the system’s output (or response) (see Figure 3.1). Based on this three-fold element concept (input, system, and output), a more strict definition of the mathematical model of a system can be given. Definition 3.1.1 The mathematical model of a system is a mathematical relation which relates the input, the system, and the output. This relation must be such as to guarantee that one can determine the system’s output for any given input. From the above definition it follows that the mathematical model is not just any relation, but a very special relation, which offers the capability of system analysis, i.e., the capability to determine the system’s response under any excitation. Furthermore, the foregoing definition reveals the basic motive for determining mathematical models. This motive is to have available appropriate tools that will facilitate the system analysis (it is well known that in order to analyze a system, we must have available its 67
68
Chapter 3
Figure 3.1
A system, its input, and its output.
mathematical model). It should be also noted that the mathematical model is useful for other purposes, as for example to study the system’s stability and other properties, to improve the system’s performance by applying control techniques, etc. 3.2
GENERAL ASPECTS OF MATHEMATICAL MODELS
The problem of determining a system’s mathematical model is essentially a problem of approximating the behavior of a physical system with an ideal mathematical expression. For example, the expression v ¼ Ri is an approximation of the physical relation between v, i, and R. To have greater accuracy, one must take into account additional factors: for example, that the resistance R changes with temperature and, consequently, the relation v ¼ Ri should have the nonlinear form v ¼ RðiÞ, where RðiÞ denotes that R is a nonlinear function of the current i. However, it is well known that this more accurate model is still an approximation of the physical system. The final conclusion is that, in general, the mathematical model can only give an approximate description of a physical system. The problem of deriving the mathematical model of a system usually appears as one of the following two cases. 1 Derivation of System’s Equations In this case, the system is considered known: for example, the network shown in Figure 3.2a. Here, we know the components R, L, C1 , and C2 and their interconnections. To determine a mathematical model of the network, one may apply Kirchhoff’s laws and write down that particular system of equations which will constitute the mathematical model. From network theory, it is well known that the system of equations sought are the linearly independent loop or node equations. 2 System Identification In this case the system is not known. By ‘‘not known,’’ we mean that we do not know either the system’s components or their interconnections. The system is just a black box. In certain cases, we may have available some a priori (in advance) useful information about the system, e.g., that the system is time-invariant or that it has lumped parameters, etc. Based on this limited information about the system, we are asked to determine a mathematical model which describes the given system ‘‘satisfactorily.’’ A well-known technique for dealing with this problem is depicted in Figure 3.2b. Here, both the physical system and the mathematical model are excited by the same input uðtÞ. Subsequently, the difference eðtÞ of the respective responses y1 ðtÞ and y2 ðtÞ is measured. If the error eðtÞ is within acceptable bounds, then the mathematical model is a satisfactory description of the system. The acceptable bounds depend on the desired degree of accuracy of the model, and they are usually stated in terms of the minimum value of the following cost function:
Mathematical Models of Systems
69
(a)
Figure 3.2a
An RLC network.
(b)
Figure 3.2b
J¼
ð t1
Schematic diagram of system identification.
e2 ðtÞ dt
t0
The area of system identification is a very interesting but very difficult engineering area. It is for this reason that it is usually taught as a special course or part of a course in the last undergraduate year or first graduate year. In this book, a very useful introduction to the problem of system identification is given in Chap. 13. 3.3
TYPES OF MATHEMATICAL MODELS
Several types of mathematical models have been proposed for the description of systems. The most popular ones, which we will present in this chapter, are the following: 1. 2. 3.
The differential equations The transfer function The impulse response
70
Chapter 3
4.
The state equations.
The main reason that all these four models will be studied here is that each model has advantages and disadvantages over the others. Hence, the knowledge of all four models offers the flexibility of using the most appropriate model among the four for a specific system or for a specific application. The above four mathematical models are based on mathematical relationships and they are presented in Secs 3.4–3.9. Note, however, that there are other ways of describing a system, aiming to give a schematic overview of the system. Two such popular schematic system descriptions are 1. 2.
The block diagrams The signal-flow graphs
These diagrams and graphs are particularly useful in giving a simplified overall picture of a system and they are presented in Secs 3.10 and 3.11, respectively. Practical examples, where all the above types of description are applied, are presented in Secs 3.12 and 3.13. In particular, Sec. 3.12 presents mathematical models of components used in control systems, such as motors, error indicators, coupled gears, etc., while Sec. 3.13 presents mathematical models of practical control systems, such as voltage control, position and velocity control, liuqid-level control, temperature control, chemical process control, etc.
3.4
DIFFERENTIAL EQUATIONS
The mathematical model of differential equations (or, more generally, of integrodifferential equations) is the oldest method of system description. This description includes all the linearly independent equations of the system, as well as the appropriate initial conditions. To illustrate this mathematical model we consider first the case of linear, timeinvariant networks. Here, the physical variables v (voltage) and i (current) in any component (e.g., R, L, and C) are related via a linear operator T (see Table 3.1). When many such elements are linked together to form a network, then the variables v and i in each element are constrained according to the following well-known Kirchhoff’s laws: 1. 2.
Kirchhoff’s voltage law. The algebraic sum of the voltages in a loop is equal to zero. Kirchhoff’s current law. The algebraic sum of the currents in a node is equal to zero.
Hence, the description of a network using differential equations consists of determining all its linearly independent differential equations. Special methods have been developed for deriving these equations and they are the main subject of a particular branch of network theory, called network topology, which provides a systematic way of choosing the independent loop and node equations. The linear time-invariant mechanical systems are another area of applications of differential equations, as a mathematical model. Here, the physical variables f (force) and v (velocity) of any component are related via a linear operator T in the
Mathematical Models of Systems
Table 3.1
71
Physical Variables in Elements of Linear Electrical Networks and Mechanical
Systems. Element
Physical variable
Linear operator T½
Inverse operator T 1 ½
Electrical Networks Resistor R
Inductor L
vðtÞ ¼ RiðtÞ
iðtÞ ¼
Current iðtÞ
T½ ¼ R½
T 1 ½ ¼
Voltage vðtÞ
vðtÞ ¼ L
d iðtÞ dt d T½ ¼ L ½ dt
Current iðtÞ
Capacitor C
1 vðtÞ R
Voltage vðtÞ
ð 1 t iðtÞ dt C 0 ðt 1 T½ ¼ ½ dt C 0 vðtÞ ¼
Voltage vðtÞ Current iðtÞ
iðtÞ ¼
1 L
1 ½ R
ðt
vðtÞ dt ð 1 t ½ dt T 1 ½ ¼ L 0 0
d vðtÞ dt d T 1 ½ ¼ C ½ dt iðtÞ ¼ C
Mechanical Systems Friction coefficient B
Mass m
Spring constant K
Force f ðtÞ
f ðtÞ ¼ BvðtÞ
Velocity vðtÞ
T½ ¼ B½
1 f ðtÞ B 1 T 1 ½ ¼ ½ B vðtÞ ¼
Velocity vðtÞ
d vðtÞ dt d T½ ¼ m ½ dt
Force f ðtÞ
f ðtÞ ¼ K
Force f ðtÞ
Velocity vðtÞ
f ðtÞ ¼ m
ðt
T½ ¼ K
vðtÞ dt ð0t 0
½ dt
vðtÞ ¼
1 m
ðt
f ðtÞ dt ð 1 t ½ dt T 1 ½ ¼ m 0 0
1 d f ðtÞ K dt 1 d T 1 ½ ¼ ½ K dt
vðtÞ ¼
same way as in an electrical network (see Table 3.1). Analogous to Kirchhoff’s laws for networks is d’Alembert’s law for mechanical systems which is stated as follows: D’Alembert’s law of forces: The sum of all forces acting upon a point mass is equal to zero. Finally, as in the case of electrical networks, in order to derive a description for a mechanical system via differential equations, one must determine all the system’s linearly independent differential equations.
72
Chapter 3
The differential equation method is demonstrated by the following examples. Example 3.4.1 Consider the network shown in Figure 3.3. Derive the network’s differential equation mathematical model. Solution Applying Kirchhoff’s voltage law, we have ð di 1 t L þ idt þ Ri ¼ vðtÞ dt C 0 The above integrodifferential equation constitutes a mathematical description of the network. To complete this description, two appropriate initial conditions must be given, since the above mathematical model is essentially a second-order differential equation. As initial conditions, we usually consider the inductor’s current iL ðtÞ and the capacitor’s voltage vC ðtÞ at the moment the switch S closes, which is usually at t ¼ 0. Therefore, the initial conditions are iL ð0Þ ¼ I0
and
vC ð0Þ ¼ V0 ;
where I0 and V0 are given constants
The integrodifferential equation and the two initial conditions constitute a complete description of the network shown in Figure 3.3. Example 3.4.2 Consider the network shown in Figure 3.4. Derive the network’s differential equation mathematical model. Solution The differential equations method for describing this network is based on the two differential equations of the first and second loop which arise by applying Kirchhoff’s voltage law. These two loop equations are
Figure 3.3
RLC network.
Mathematical Models of Systems
Figure 3.4
73
A two-loop network.
ð ð 1 t 1 t i1 ðtÞ dt i ðtÞ dt ¼ vðtÞ C 0 C 0 2 ð ð 1 t di 1 t i1 ðtÞ dt þ R2 i2 ðtÞ þ L 2 þ i ðtÞ dt ¼ 0 C 0 dt C 0 2
R1 i1 ðtÞ þ
with initial conditions vc ð0Þ ¼ V0 and iL ð0Þ ¼ I0 . Example 3.4.3 Consider the mechanical system shown in Figure 3.5, where y, K, m, and B are the position of the mass, the spring’s constant, the mass, and the friction coefficient, respectively. Derive the system’s differential equation mathematical model. Solution By using d’Alembert’s law of forces, the following differential equation is obtained
Figure 3.5
A spring and a mass.
74
Chapter 3
m
d2 y dy þ B þ Ky ¼ f ðtÞ 2 dt dt
The initial conditions of the above equation are the distance yðtÞ and the velocity vðtÞ ¼ dy=dt at the moment t ¼ 0, i.e., at the moment where the external force f ðtÞ is applied. Therefore the initial conditions are dy yð0Þ ¼ Y0 and vð0Þ ¼ ¼ V0 ; where Y0 and V0 are given constants dt t¼0 The differential equation and the two initial conditions constitute a complete description of the mechanical system shown in Figure 3.5 using the method of differential equations. Remark 3.4.1 The method of differential equations is a description in the time domain which can be applied to many categories of systems, such as linear and non linear, time-invariant and time-varying, with lumped and distributed parameters, with zero and nonzero initial conditions and many others. 3.5
TRANSFER FUNCTION
In contrast to the differential equation method which is a description in the time domain, the transfer function method is a description in the frequency domain and holds only for a restricted category of systems, i.e., for linear time-invariant systems having zero initial conditions. The transfer function is designated by HðsÞ and is defined as follows: Definition 3.5.1 The transfer function HðsÞ of a linear, time-invariant system with zero initial condtiions is the ratio of the Laplace transform of the output yðtÞ to the Laplace transform of the input uðtÞ, i.e., HðsÞ ¼
Lf yðtÞg YðsÞ ¼ LfuðtÞg UðsÞ
ð3:5-1Þ
The introductory examples used in Sec. 3.4 will also be used here for the derivation of their transfer function. Example 3.5.1 Consider the network shown in Figure 3.3. Derive the transfer function HðsÞ ¼ IðsÞ=VðsÞ. Solution This network, in the frequency domain and with zero initial conditions I0 and V0 , is as shown in Figure 3.6. To determine the transfer function HðsÞ ¼ IðsÞ=VðsÞ, we work as follows: From Kirchhoff’s voltage law, we have LsI ðsÞ þ RIðsÞ þ
IðsÞ ¼ VðsÞ Cs
Mathematical Models of Systems
Figure 3.6
75
RLC circuit.
Therefore, the transfer function sought is given by HðsÞ ¼
IðsÞ IðsÞ Cs ¼ ¼ 2 1 VðsÞ LCs þ RCs þ 1 Ls þ R þ IðsÞ Cs
Furthermore, consider as circuit output the voltage VR ðsÞ across the resistor. In this case, the transfer function becomes HðsÞ ¼
VR ðsÞ RIðsÞ RCs ¼ ¼ VðsÞ VðsÞ LCs2 þ RCs þ 1
Example 3.5.2 Consider the electrical network shown in Figure 3.4. Determine the transfer function HðsÞ ¼ I2 ðsÞ=VðsÞ. Solution This network, in the frequency domain and with zero initial conditions, is as shown in Figure 3.7. To determine HðsÞ ¼ I2 ðsÞ=VðsÞ, we start by writing the two loop equations: 1 1 R1 þ I1 ðsÞ I2 ðsÞ ¼ VðsÞ Cs Cs 1 1 I1 ðsÞ þ R2 þ Ls þ I ðsÞ ¼ 0 Cs Cs 2 Next, solve the system for I2 ðsÞ. The second equation yields I1 ðsÞ ¼ ½LCs2 þ R2 Cs þ 1I2 ðsÞ Substituting this result in the first equation, we have ðR1 Cs þ 1ÞðLCs2 þ R2 Cs þ 1ÞI2 ðsÞ I2 ðsÞ ¼ CsVðsÞ Hence
76
Chapter 3
Figure 3.7
A two-loop network.
I2 ðsÞ Cs ¼ VðsÞ ðR1 Cs þ 1ÞðLCs2 þ R2 Cs þ 1Þ 1 1 ¼ 2 R1 LCs þ ðR1 R2 C þ LÞs þ R1 þ R2
HðsÞ ¼
Example 3.5.3 Consider the mechanical system shown in Figure 3.5. Determine the transfer function HðsÞ ¼ YðsÞ=FðsÞ. Solution This system, in the frequency domain and with zero initial conditions, will be as shown in Figure 3.8. In order to determine the transfer function HðsÞ ¼ YðsÞ=FðsÞ, we first write down the d’Alembert’s law of forces. The result is ms2 YðsÞ þ BsYðsÞ þ KYðsÞ ¼ FðsÞ
Figure 3.8
A spring and a mass.
Mathematical Models of Systems
77
Therefore HðsÞ ¼
YðsÞ 1 ¼ FðsÞ ms2 þ Bs þ K
Remark 3.5.1 In the above examples, we observe that the transfer function HðsÞ is the ratio of two polynomials in the freqency domain. In general, HðsÞ has the following form m Y ðs þ zi Þ
HðsÞ ¼
bðsÞ bm sm þ bm1 sm1 þ þ b1 s þ b0 ¼ ¼ K i¼1 n Y aðsÞ sn þ an1 sn1 þ þ a1 s þ a0
ðs þ pi Þ
i¼1
where pi are the roots of the denominator’s polynomial aðsÞ of HðsÞ and are called the poles of HðsÞ, and zi are the roots of the numerator’s polynomial bðsÞ of HðsÞ and are called the zeros of HðsÞ. Poles and zeros, particularly the poles, play a significant role in the behavior of a system. This fact will most often be demonstrated in many places of this book. 3.6
IMPULSE RESPONSE
Impulse response is a time-domain description and holds only for a limited category of systems; i.e., the linear time-invariant and time-varying systems having zero initial conditions. The impulse response is designated by hðtÞ and is defined as follows: Definition 3.6.1 The impulse response hðtÞ of a linear system with zero initial conditions is the system’s output when its input is the unit impulse function ðtÞ (see Eq. (2.2-3) of Chap. 2). Consider the special case of linear, time-invariant systems where the relation YðsÞ ¼ HðsÞUðsÞ holds. Let the input be the unit impulse function, i.e., let uðtÞ ¼ ðtÞ. Then, since UðsÞ ¼ 1, we readily have that YðsÞ ¼ HðsÞ. Therefore, the transfer function HðsÞ and the impulse response hðtÞ are related as follows: HðsÞ ¼ LfhðtÞg
or
hðtÞ ¼ L1 fHðsÞg
ð3:6-1Þ
The two relations in Eq. (3.6-1) show that for time-invariant systems, HðsÞ and hðtÞ are essentially the same description; whereas HðsÞ is in the frequency domain, hðtÞ is in the time domain. Also, Eq. (3.6-1) suggests an easy way of passing from one description to the other. Actually, if HðsÞ is known, then hðtÞ ¼ L1 fHðsÞg and vice versa; if hðtÞ is known, then HðsÞ ¼ LfhðtÞg. Therefore, one way of determining the impulse response of a linear, timeinvariant system, provided that its HðsÞ is known, is to use the relation hðtÞ ¼ L1 fHðsÞg. Another way of determining the impulse response is through the system’s differential equation. In this case, let the system’s differential equation have the general form an yðnÞ þ an1 yðn1Þ þ þ a1 yð1Þ þ a0 y ¼ uðtÞ
ð3:6-2Þ
78
Chapter 3
Then it can be readily shown that the solution of the homogeneous equation an yðnÞ þ an1 yðn1Þ þ þ a1 yð1Þ þ a0 y ¼ 0
ð3:6-3Þ
with initial conditions yð0Þ ¼ yð1Þ ð0Þ ¼ ¼ yðn2Þ ð0Þ ¼ 0
and
yðn1Þ ð0Þ ¼ 1=an
ð3:6-4Þ
is the system’s impulse response.
3.7 3.7.1
STATE EQUATIONS General Introduction
State-space equations, or simply state equations, is a description in the time domain which may be applied to a very wide category of systems, such as linear and nonlinear systems, time-invariant and time-varying systems, systems with nonzero initial conditions, and others. The term state of a system refers to the past, present, and future of the system. From the mathematical point of view, the state of a system is expressed by its state variables. Usually, a system is described by a finite number of state variables, which are designated by x1 ðtÞ; x2 ðtÞ; . . . ; xn ðtÞ and are defined as follows: Definition 3.7.1 The state variables x1 ðtÞ; x2 ðtÞ; . . . ; xn ðtÞ of a system are defined as a (minimum) number of variables such that if we know (a) their values at a certain moment t0 , (b) the input function applied to the system for t t0 , and (c) the mathematical model which relates the input, the state variables, and the system itself, then the determination of the system’s states for t > t0 is guaranteed. Consider a system with many inputs and many outputs, as shown in Figure 3.9. The input vector is designated by uðtÞ and has the form
Figure 3.9
System with many inputs and many outputs.
Mathematical Models of Systems
3 u1 ðtÞ 6 u2 ðtÞ 7 7 6 uðtÞ ¼ 6 . 7 4 .. 5
79
2
ð3:7-1Þ
um ðtÞ
where m is the number of inputs. The output vector is designated by yðtÞ and has the form 3 2 y1 ðtÞ 6 y2 ðtÞ 7 7 6 ð3:7-2Þ yðtÞ ¼ 6 .. 7 4 . 5 yp ðtÞ where p is the number of outputs. The state vector is designated by xðtÞ and has the form 3 2 x1 ðtÞ 6 x2 ðtÞ 7 7 6 xðtÞ ¼ 6 . 7 ð3:7-3Þ 4 .. 5 xn ðtÞ
where n is the number of the state variables. The state equations are a set of n first-order differential equations which relate the input vector uðtÞ with the state vector xðtÞ and has the form ¼ f½xðtÞ; uðtÞ xðtÞ ð3:7-4Þ where f is a column with n elements. The function f is, in general, a complex nonlinear function of xðtÞ and uðtÞ. Note that Eq. (3.7-4) is a set of dynamic equations. The output vector yðtÞ of the system is related to the input vector uðtÞ and the state vector xðtÞ as follows: yðtÞ ¼ g½xðtÞ; uðtÞ
ð3:7-5Þ
where g is a column with p elements. Relation (3.7-5) is called the output equation. The function g is generally a complex nonlinear function of xðtÞ and uðtÞ. Note that Eq. (3.7-5) is a set of algebraic (nondynamic) equations. The initial conditions of the state-space equations (3.7-4) are the values of the elements of the state vector xðtÞ for t ¼ t0 and are denoted as 3 2 x1 ðt0 Þ 6 x2 ðt0 Þ 7 6 7 ð3:7-6Þ xðt0 Þ ¼ x0 ¼ 6 . 7 4 .. 5 xn ðt0 Þ
The state-space equations (3.7-4), the output equation (3.7-5), and the initial conditions (3.7-6), i.e., the following equations ¼ f½xðtÞ; uðtÞ xðtÞ ð3:7-7aÞ yðtÞ ¼ g½xðtÞ; uðtÞ
ð3:7-7bÞ
xðt0 Þ ¼ x0
ð3:7-7cÞ
80
Chapter 3
constitute the description of a dynamic system in state space. Due to the fact that the dynamic state equations (3.7-7a) dominate in the three sets of equations (3.7-7a–c), in the sequel, all three equations in (3.7-7a–c), will be called, for simplicity, state equations. The state equations (3.7-7) are, for the field of automatic control systems, the modern method of system description. This method has special theoretical, computational, and practical importance for the following main reasons: 1.
2.
3.
4.
State equations can describe a large category of systems, such as linear and nonlinear systems, time-invariant and time-varying systems, systems with time delays, systems with nonzero initial conditions, and others. Due to the fact that state equations are a set of first-order differential equations, they can be easily programmed and simulated on both digital and analog computers. State equations, by their very nature, greatly facilitate both in formulating and subsequently in investigating a great variety of properties in system theory, such as stability, controllability, and observability. They also facilitate the study of fundamental control problems, such as pole placement, optimal and stochastic control, and adaptive and robust control. State equations provide a more complete description of a system than the other three methods: i.e., differential equations, transfer function, and impulse response. This is because state equations involve very important additional information about the system—namely, the system’s state. This information is particularly revealing of the system’s structure (e.g., regarding controllability, observability, pole-zero cancellation in the transfer function, etc.).
Overall, state equations is a description which relates the following four elements: input, system, state variables, and output. In contrast, the differential equations, the transfer function, and the impulse response relate three elements: input, system, and output—wherein the input is related to the output via the system directly (i.e., without giving information about the state of the system). It is exactly for this reason that these three system descriptions are called input–output descriptions.
3.7.2
Description of Linear Systems via State Equations
If a linear, time-invariant system can be described by a set of ordinary differential equations, then the state equations (3.7-7) take on the following special form: ¼ AxðtÞ þ BuðtÞ xðtÞ
ð3:7-8aÞ
yðtÞ ¼ CxðtÞ þ DuðtÞ
ð3:7-8bÞ
xðt0 Þ ¼ xð0Þ ¼ x0
ð3:7-8cÞ
Matrix A has dimensions n n and it is called the system matrix, having the general form
Mathematical Models of Systems
2
a11 6 a21 6 A ¼ 6 .. 4 .
a12 a22 .. .
an1
an2
3 a1n a2n 7 7 .. 7 . 5
81
ð3:7-9Þ
ann
Matrix B has dimensions n m and it is called the input matrix, having the general form 3 2 b11 b12 b1m 6 b21 b22 b2m 7 6 7 ð3:7-10Þ B ¼ 6 .. .. 7 .. 4 . . 5 . bn1
bn2
bnm
Matrix C has dimensions p n and it is called the output matrix, having the general form 2 3 c11 c12 c1n 6 c21 c22 c2n 7 7 6 ð3:7-11Þ C ¼ 6 .. .. 7 .. 4 . . 5 . cp1 cp2 cpn Matrix D has dimensions p m and it is called the feedforward matrix, having the general form 3 2 d11 d12 d1m 6 d21 d22 d2m 7 7 6 D ¼ 6 .. ð3:7-12Þ .. 7 .. 4 . . 5 . dp1 dp2 dpm Depending on the dimensions m and p of the input and output vectors, we have the following four categories of systems: 1. Multi-input–multi-output systems (MIMO). In this case m > 1 and p > 1, and the system is described by Eqs (3.7-8). 2. Multi-input–single-output systems (MISO). In this case m > 1 and p ¼ 1, and the system is described by the equations ¼ AxðtÞ þ BuðtÞ xðtÞ ð3:7-13aÞ yðtÞ ¼ cT xðtÞ þ dT uðtÞ
ð3:7-13bÞ
xð0Þ ¼ x0
ð3:7-13cÞ
where c and d are column vectors with n and m elements, respectively. 3. Single-input–multi-output systems (SIMO). In this case m ¼ 1 and p > 1, and the system is described by the equations ¼ AxðtÞ þ buðtÞ xðtÞ ð3:7-14aÞ yðtÞ ¼ CxðtÞ þ duðtÞ
ð3:7-14bÞ
xð0Þ ¼ x0
ð3:7-14cÞ
where b and d are column vectors with n and p elements, respectively.
82
Chapter 3
4. Single-input–single-output systems (SISO). In this case m ¼ p ¼ 1 and the system is described by the equations ¼ AxðtÞ þ buðtÞ xðtÞ ð3:7-15aÞ yðtÞ ¼ cT xðtÞ þ duðtÞ
ð3:7-15bÞ
xð0Þ ¼ x0
ð3:7-15cÞ
where d is a scalar quantity. It is noted that very often in the literature, for the sake of brevity, system (3.78a,b) is denoted by ½A; B; C; Dn . If a linear time-varying system can be described by a set of linear differential equations, then the state equations (3.7-7) take on the form ¼ AðtÞxðtÞ þ BðtÞuðtÞ xðtÞ ð3:7-16aÞ yðtÞ ¼ CðtÞxðtÞ þ DðtÞuðtÞ
ð3:7-16bÞ
xðt0 Þ ¼ x0
ð3:7-16cÞ
where the time-varying matrices AðtÞ, BðtÞ, CðtÞ, and DðtÞ are defined similarly to the constant matrices A, B, C, and D of system (3.7-8). 3.7.3
Transfer Function and Impulse Response Matrices
1 Transfer Function Matrix In studying linear, time-invariant systems described by state equations (3.7-8), one may use the Laplace transform. Indeed, applying the Laplace transform to Eqs (3.78a,b) yields sXðsÞ xð0Þ ¼ AXðsÞ þ BUðsÞ
ð3:7-17aÞ
YðsÞ ¼ CXðsÞ þ DUðsÞ
ð3:7-17bÞ
where XðsÞ ¼ LfxðtÞg, UðsÞ ¼ LfuðtÞg and YðsÞ ¼ Lf yðtÞg. From Eq. (3.7-17a), we have XðsÞ ¼ ðsI AÞ1 BUðsÞ þ ðsI AÞ1 xð0Þ
ð3:7-18Þ
If we substitute Eq. (3.7-18) in Eq. (3.7-17b), we have YðsÞ ¼ ½CðsI AÞ1 B þ DUðsÞ þ CðsI AÞ1 xð0Þ
ð3:7-19Þ
Equation (3.7-19) is an expression of the output vector YðsÞ as a function of the input vector UðsÞ and of the initial condition vector xð0Þ. When the initial condition vector xð0Þ ¼ 0, then Eq. (3.7-19) reduces to YðsÞ ¼ ½CðsI AÞ1 B þ DUðsÞ
ð3:7-20Þ
Obviously, Eq. (3.7-20) is an input–output relation, since it directly relates the input vector UðsÞ with the output vector YðsÞ (the state vector XðsÞ has been eliminated). Equation (3.7-20) is the same as Eq. (3.5-1), except that Eq. (3.5-1) relates scalars, while Eq. (3.7-20) relates vectors. This difference is because Eq. (3.5-1) holds for SISO systems, while Eq. (3.7-20) holds for MIMO systems. Therefore, Eq. (3.5-1) is a special case of Eq. (3.7-20), where m (number of inputs) ¼ p (number of out-
Mathematical Models of Systems
83
puts) ¼ 1. The transfer function matrix HðsÞ of system (3.7-8) is analogous to the scalar transfer function HðsÞ of Eq. (3.5-1) and has the following form HðsÞ ¼ CðsI AÞ1 B þ D
ð3:7-21Þ
The transfer function matrix HðsÞ has dimensions p m and has the general form
2
h11 ðsÞ h12 ðsÞ 6 h21 ðsÞ h22 ðsÞ 6 HðsÞ ¼ 6 .. .. 4 . . hp1 ðsÞ hp2 ðsÞ
3 h1m ðsÞ h2m ðsÞ 7 7 .. 7 . 5
ð3:7-22Þ
hpm ðsÞ
Each element hij ðsÞ of HðsÞ is a scalar transfer function which relates the element yi ðsÞ of the output vector YðsÞ with the element uj ðsÞ of the input vector UðsÞ, provided that all the other elements of the ith row of HðsÞ are zero. In general, we have yi ðsÞ ¼
m X
hij ðsÞuj ðsÞ;
i ¼ 1; 2; . . . ; p
ð3:7-23Þ
j¼1
To give a simplified view of Eq. (3.7-23), consider the special case of a two input–two output system, in which case relation YðsÞ ¼ HðsÞUðsÞ has the form y1 ðsÞ ¼ h11 ðsÞu1 ðsÞ þ h12 ðsÞu2 ðsÞ y2 ðsÞ ¼ h21 ðsÞu1 ðsÞ þ h22 ðsÞu2 ðsÞ The block diagram (see Sec. 3.10) of these two equations is shown in Figure 3.10. 2 Impulse Response Matrix The impulse response matrix HðtÞ is analogous to the scalar impulse function hðtÞ given by Definition 3.6.1. Using Eq. (3.6-1) we readily have LfHðtÞg ¼ HðsÞ
or
HðtÞ ¼ L1 fHðsÞg
The impulse response matrix HðtÞ 2 h11 ðtÞ h12 ðtÞ 6 h21 ðtÞ h22 ðtÞ 6 HðtÞ ¼ 6 .. .. 4 . . hp1 ðtÞ hp2 ðtÞ
has a general form 3 h1m ðtÞ h2m ðtÞ 7 7 .. 7 . 5 hpm ðtÞ
ð3:7-24Þ
ð3:7-25Þ
Therefore, the element hij ðtÞ ¼ L1 fhij ðsÞg is the scalar impulse response between the element yi ðtÞ of the output vector yðtÞ and the element uj ðtÞ of the input vector uðtÞ, provided that all the other vectors of the ith row of HðtÞ are zero. Example 3.7.1 Consider the network shown in Figure 3.11 with initial condition iL ð0Þ ¼ I0 . Determine the state equations of the network. Solution The loop equation is
84
Chapter 3
Figure 3.10
L
Block diagram of a two input–two output system.
di þ Ri ¼ vðtÞ dt
or
di R 1 ¼ i þ vðtÞ dt L L
Define as state variable xðtÞ of the network the physical quantity xðtÞ ¼ iL ðtÞ ¼ the inductor’s current. The system output is the voltage across the resistor R. As a result, the network’s description via state equations is x_ ðtÞ ¼
R 1 xðtÞ þ vðtÞ L L
vR ðtÞ ¼ R xðtÞ iL ð0Þ ¼ ið0Þ ¼ I0
Figure 3.11
RL network.
Mathematical Models of Systems
85
Since the network is a SISO system, the above state equations have the same form as that of Eqs (3.7-15), with the exception that in this case all values are scalar. This is because the network is a first-order system, and hence there is only one state variable ðn ¼ 1Þ. Example 3.7.2 Consider the network shown in Figure 3.3, with initial conditions iL ð0Þ ¼ I0 and vC ð0Þ ¼ V0 . Determine the state equations of the network. Solution The loop equation is ð di 1 t L þ Ri þ idt ¼ vðtÞ dt C 0 Define as state variables x1 ðtÞ and x2 ðtÞ of the network the following physical quantities: x1 ðtÞ ¼ iL ðtÞ ¼ the inductor’s current ðt x2 ðtÞ ¼ idt ¼ the capacitor’s electrical charge 0
By differentiating the last equation, the following relation is obtained: x_ 2 ðtÞ ¼ iðtÞ ¼ iL ðtÞ ¼ x1 ðtÞ If the state variables are inserted in the loop equation, it yields Lx_ 1 ðtÞ þ Rx1 ðtÞ þ
1 x ðtÞ ¼ vðtÞ C 2
or x_ 1 ðtÞ ¼
R 1 1 x1 ðtÞ x2 ðtÞ þ vðtÞ L LC L
The above relation, together with the relation x_ 2 ðtÞ ¼ x1 ðtÞ, gives the following system of first-order differential equations: 2 3 2 3 R 1 1 x1 ðtÞ x_ 1 ðtÞ ¼4 L þ 4 L 5vðtÞ LC 5 x2 ðtÞ x_ 2 ðtÞ 1 0 0 Let the network’s output be the voltage vR ðtÞ across the resistor R. Then, the network’s output equation is x ðtÞ yðtÞ ¼ vR ðtÞ ¼ RiðtÞ ¼ Rx1 ðtÞ ¼ ½R 0 1 x2 ðtÞ The initial condition vector of the network is iL ð0Þ I0 x1 ð0Þ ¼ ¼ xð0Þ ¼ CV0 x2 ð0Þ CvC ð0Þ Since the network is a SISO system, the state equations sought have the form of Eqs (3.7-15), where
86
Chapter 3
xðtÞ ¼ 2
R A¼4 L 1
i ðtÞ x1 ð0Þ ¼ L ; x2 ð0Þ vC ðtÞ
3 1 LC 5; 0
3 1 b ¼ 4 L 5; 0
uðtÞ ¼ vðtÞ;
2
c¼
R 0
;
yðtÞ ¼ vR ðtÞ x0 ¼
I0
CV0
Example 3.7.3 Consider the mechanical system shown in Figure 3.5 with initial conditions yð0Þ ¼ Y0 and yð1Þ ð0Þ ¼ U0 , where Y0 is the initial position and U0 is the initial velocity of the mass. Determine the state equations of the system. Solution The d’Alembert’s law of forces is as follows: m
d2 y dy þ B þ Ky ¼ f ðtÞ dt dt2
Define as state variables: x1 ðtÞ ¼ yðtÞ ¼ the mass displacement and x2 ðtÞ ¼ y_ðtÞ ¼ the mass velocity. We notice that x_ 1 ðtÞ ¼ x2 ðtÞ. By substituting the state variables in d’Alembert’s equation, we have mx_ 2 ðtÞ þ Bx2 ðtÞ þ Kx1 ðtÞ ¼ f ðtÞ or x_ 2 ðtÞ ¼
K B 1 x ðtÞ x2 ðtÞ þ f ðtÞ m 1 m m
The above equation, together with the relation x_ 1 ðtÞ ¼ x2 ðtÞ, may be written as the following system of first-order differential equations: # " # " 0 0 1 x ðtÞ x_ 1 ðtÞ 1 K B ¼ þ 1 f ðtÞ x2 ðtÞ x_ 2 ðtÞ m m m Let yðtÞ be the displacement of the mass m. Then, the system’s output equation will be x1 ðtÞ yðtÞ ¼ ½1 0 x2 ðtÞ The initial condition vector is yð0Þ Y0 x1 ð0Þ ¼ ¼ xð0Þ ¼ y_ð0Þ U0 x2 ð0Þ Hence, the state equations of the mechanical system have the form of Eqs (3.7-15), where
Mathematical Models of Systems
yðtÞ x1 ðtÞ ¼ ; uðtÞ ¼ f ðtÞ y_ ðtÞ x2 ðtÞ " # " # 0 0 1 1 K B ; A¼ c¼ ; b¼ 1 ; 0 m m m
87
xðtÞ ¼
3.8
x0 ¼
Y0
U0
TRANSITION FROM ONE MATHEMATICAL MODEL TO ANOTHER
As pointed out in Sec. 3.3, every mathematical model has advantages and disadvantages over others: to take advantage of the advantages of all mathematical models, one must have the flexibility of going from one model to another. The issue of transition from one mathematical model to another is obviously of great practical and theoretical importance. In the sequel, we present some of the most interesting such cases. 3.8.1
From Differential Equation to Transfer Function for SISO Systems
Case 1. The Right-Hand Side of the Differential Equation Does Not Involve Derivatives Consider a SISO system described by the differential equation yðnÞ þ an1 yðn1Þ þ þ a1 yð1Þ þ a0 y ¼ b0 uðtÞ
ð3:8-1Þ ðkÞ
where all the system’s initial conditions are assumed zero, i.e., y ð0Þ ¼ 0 for k ¼ 1; 2; . . . ; n 1. Applying the Laplace transform to Eq. (3.8-1), we obtain sn YðsÞ þ an1 sn1 YðsÞ þ a1 sYðsÞ þ a0 YðsÞ ¼ b0 UðsÞ Hence, the transfer function sought is given by HðsÞ ¼
YðsÞ b0 ¼ UðsÞ sn þ an1 sn1 þ þ a1 s þ a0
ð3:8-2Þ
Case 2. The Right-Hand Side of the Differential Equation Involves Derivatives Consider a SISO system described by the differential equation yðnÞ þ an1 yðn1Þ þ þ a1 yð1Þ þ a0 y ¼ bm uðmÞ þ þ b1 uð1Þ þ b0 u
ð3:8-3Þ ðkÞ
where m < n and where all initial conditions are assumed zero, i.e., y ð0Þ ¼ 0, for k ¼ 0; 1; . . . ; n 1. We can determine the transfer function of Eq. (3.8-3) as follows: Let zðtÞ be the solution of Eq. (3.8-1), with b0 ¼ 1. Then, using the superposition principle, the solution yðtÞ of Eq. (3.8-3) will be yðtÞ ¼ bm zðmÞ þ bm1 zðm1Þ þ þ b1 zð1Þ þ b0 z
ð3:8-4Þ
Applying the Laplace transformation to Eq. (3.8-4) we obtain YðsÞ ¼ bm sm ZðsÞ þ bm1 sm1 ZðsÞ þ þ b1 sZðsÞ þ b0 ZðsÞ
ð3:8-5Þ
88
Chapter 3
Here, we have set zðkÞ ð0Þ ¼ 0, for k ¼ 0; 1; . . . ; n 1, since the solution of Eq. (3.8-1), when b0 ¼ 1, is zðtÞ ¼ yðtÞ, where it was assumed that all initial conditions of yðtÞ (and hence of zðtÞ) are zero. In Eq. (3.8-2), if b0 ¼ 1, then 1 UðsÞ ZðsÞ ¼ n s þ an1 sn1 þ þ a1 s þ a0 By substituting the above expression of ZðsÞ in Eq. (3.8-5), the transfer function HðsÞ of the differential equation (3.8-3) is obtained; it has the form HðsÞ ¼
YðsÞ bm sm þ bm1 sm1 þ þ b1 s þ b0 ¼ n UðsÞ s þ an1 sn1 þ þ a1 s þ a0
ð3:8-6Þ
Remark 3.8.1 The transfer function HðsÞ given by Eq. (3.8-6) can be easily derived from Eq. (3.8-3) if we set sk in place of the kth derivative and replace yðtÞ and uðtÞ with YðsÞ and UðsÞ, respectively; i.e., we derive Eq. (3.8-6) by replacing yðkÞ ðtÞ by sk YðsÞ and uðkÞ ðtÞ by sk UðsÞ in Eq. (3.8-3). 3.8.2
From Transfer Function to Differential Equation for SISO Systems
Let a SISO system be described by Eq. (3.8-6). Then, working backwards the method given in Remark 3.8.1, the differential equation (3.8-3) can be constructed by substituting sk by the kth derivative and YðsÞ and UðsÞ with yðtÞ and uðtÞ, respectively. 3.8.3
From HðsÞ to HðtÞ and Vice Versa
The matrices HðsÞ and HðtÞ, according to Eqs (3.6-1) and (3.7-24) are related through the Laplace transform. Therefore, the following transition relations may be used: LfHðtÞg ¼ HðsÞ
3.8.4
or
HðtÞ ¼ L1 fHðsÞg
ð3:8-7Þ
From State Equations to Transfer Function Matrix
Consider a system described by the state equations (3.7-8a,b). Then the system’s transfer function matrix HðsÞ is given by (3.7-21), i.e., by the relation HðsÞ ¼ CðsI AÞ1 B þ D
3.8.5
ð3:8-8Þ
From Transfer Function Matrix to State Equations
The transition from HðsÞ to state equations is the well-known problem of state-space realization. This is, in general, a difficult problem and has been, and still remains, a topic of research. In the sequel, we will present some introductory results regarding this problem.
Mathematical Models of Systems
89
Case 1. SISO Systems Let a system be described by a scalar transfer function of the form HðsÞ ¼
YðsÞ bn1 sn1 þ bn2 sn2 þ þ b1 s þ b0 ¼ UðsÞ sn þ an1 sn1 þ þ a1 s þ a0
ð3:8-9Þ
or, equivalently, by the differential equation yðnÞ þ an1 yðn1Þ þ þ a1 yð1Þ þ a0 y ¼ bn1 uðn1Þ þ bn2 uðn2Þ þ þ b1 uð1Þ þ b0 u
ð3:8-10Þ
Equation (3.8-10) can be expressed in the form of two equations as follows: zðnÞ þ an1 zðn1Þ þ þ a1 zð1Þ þ a0 z ¼ u
ð3:8-11Þ
yðtÞ ¼ bn1 zðn1Þ þ þ b1 zð1Þ þ b0 z
ð3:8-12Þ
Let zðtÞ be the solution of Eq. (3.8-11). Then, the solution of Eq. (3.8-10) will be given by Eq. (3.8-12), where use is made of the superposition principle. The state variables x1 ; x2 ; . . . ; xn are defined as follows: x1 ðtÞ ¼ zðtÞ x2 ðtÞ ¼ zð1Þ ðtÞ
¼ xð1Þ 1 ðtÞ
x3 ðtÞ ¼ zð2Þ ðtÞ .. .. . .
¼ xð1Þ 2 ðtÞ .. .
ð3:8-13Þ
xn ðtÞ ¼ zðn1Þ ðtÞ ¼ xð1Þ n1 ðtÞ If we substitute Eqs (3.8-13) into Eq. (3.8-11), we have x_ n ðtÞ ¼ an1 xn ðtÞ a1 x2 ðtÞ a0 x1 ðtÞ þ uðtÞ
ð3:8-14Þ
Also, if we substitute Eqs (3.8-13) into (3.8-12), we have yðtÞ ¼ bn1 xn ðtÞ þ bn2 xn1 ðtÞ þ þ b1 x2 ðtÞ þ b0 x1 ðtÞ
ð3:8-15Þ
Equations (3.8-13)–(3.8-15) can be expressed in a matrix form, as follows: ¼ AxðtÞ þ buðtÞ xðtÞ ð3:8-16aÞ yðtÞ ¼ cT xðtÞ
ð3:8-16bÞ
where x ¼ ðx1 ; x2 ; . . . ; xn Þ and 2 0 1 0 6 0 0 1 6 . .. .. .. A¼6 . . 6 4 0 0 0 T
a0
a1
a2
0 0 .. .
0 a3
0 0 .. . 1 an1
3 7 7 7; 7 5
2 3 0 607 6.7 .7 b¼6 6 . 7; 405 1
2
b0 b1 .. .
3
7 6 7 6 7 6 c¼6 7 7 6 4 bn2 5 bn1 ð3:8-17Þ
Hence, Eqs (3.8-16) constitute the state equations’ description of the transfer function (3.8-9).
90
Chapter 3
Due to the special form of matrix A and of vector b, wherein there are only ones and zeros, except in the last line in A and b, we say that the state equations (3.816) are in phase canonical form, while the state variables are called phase variables. Phase variables are, in general, state variables which are defined according to Eqs (3.8-13); i.e., every state variable is the derivative of the previous one. In particular, the special form of matrix A and of vector b is characterized by as follows: in matrix A, if the first column and the last row are deleted, then a ðn 1Þ ðn 1Þ unit matrix is revealed. Also, the elements of the last row of A are the coefficients of the differential equation (3.8-10), placed in reverse order and all having negative signs. The vector b has all its elements equal to zero, except for the nth element, which is equal to one. The block diagram of system (3.8-16) is given in Figure 3.12. Case 2. MIMO Systems To determine a state-space realization of a MIMO system, where the dimension n of the state vector xðtÞ is the smallest possible (i.e., n is minimum), is a rather difficult task and is beyond the scope of this book. However, certain types of nonminimal realizations are easy to attain, as shown in the sequel. Let a system be described by the transfer function matrix HðsÞ, with dimensions p m, having the general form 3 2 h11 ðsÞ h12 ðsÞ h1m ðsÞ 6 h21 ðsÞ h22 ðsÞ h2m ðsÞ 7 6 7 ð3:8-18Þ HðsÞ ¼ 6 .. .. 7 .. 4 . . 5 . hp1 ðsÞ hp2 ðsÞ
hpm ðsÞ
where hij ðsÞ ¼ ij ðsÞ=pij ðsÞ, and the degree of the polynomial ij ðsÞ is smaller than that of pij ðsÞ. Let
Figure 3.12 SISO system.
Block diagram of the state equations (3.18-16) in phase canonical form of a
Mathematical Models of Systems
91
pðsÞ ¼ sn þ pn1 sn1 þ þ p1 s þ p0 be the least common multiplier polynomial of all pij ðsÞ. Then the product pðsÞHðsÞ is a matrix polynomial which can be expressed as follows: pðsÞHðsÞ ¼ H0 þ H1 s þ þ Hn1 sn1 Let Om be a m m zero matrix, i.e., a matrix with all its elements equal to zero and Im be a m m unit matrix. Then, the following state equations constitute a description of Eq. (3.8-18) in state space: x ¼ Ax þ Bu ð3:8-19aÞ y ¼ Cx where
2
ð3:8-19bÞ
Om Om .. .
6 6 6 A¼6 6 4 Om p0 Im
Im Om .. .
Om Im .. .
Om p1 Im
Om p2 Im
Om Om .. . Im pn1 Im
3 7 7 7 7; 7 5
3 Om 6 Om 7 7 6 6 . 7 B ¼ 6 .. 7 7 6 4 Om 5 Im 2
and C ¼ ½H0 j H1 j j Hn2 j Hn1 The dimensions of x, u, y, A, B, and C are nm 1, m 1, p 1, nm nm, nm m, and p nm, respectively. It is obvious that the results of Case 1 are a special case of the present case, since if we set m ¼ p ¼ 1 in Eqs (3.8-19), then we readily derive system (3.8-16). In system realization, i.e., when going from HðsÞ to state equations, several problems arise. We mention the following: 1. The Problem of Realization This problem examines the existence of a description in state space. Practically, this means that it is not always possible for any given transfer function, or more generally, for any given input–output description, to be able to determine a description in state space of the form (3.7-8). 2. The Problem of Minimum Dimension n This problem refers to the determination of a state-space realization involving the minimum possible number of state variables. Such a realization is called a minimal realization. For example, realization (3.8-16) is a minimal realization, while (3.8-19), in general, is not. 3. The Problem of Minimum Number of Parameters This problem refers to the determination of a state-space realization in which the number of parametric elements in the matrices A, B, and C are as minimum as possible. Realization (3.8-16) is a minimum parameter realization. This is due to the special form of A and b, where A has only n parametric elements (the n elements of its last row), while b has none. Note that the elements 0 and 1 in A and b are not parametric, since they do not depend on the parameters of the system.
92
3.9
Chapter 3
EQUIVALENT STATE-SPACE MODELS
The transition from one description to another may or may not be unique. For example, the transition from a state-space description to its input–output description HðsÞ or HðtÞ is unique. On the contrary, the transition from an input–output description to a state-space model is never unique. That is, the realization problem does not have a unique solution. Hence, the state equations (3.8-16) are one of the many possible descriptions of the transfer function (3.8-9) in state space. The same stands for the state-space realization (3.8-19) of the transfer function matrix (3.8-18). The nonuniqueness in state-space realization generates the problem of description equivalence, examined in the sequel. Definition 3.9.1 Systems ½A; B; C; Dn and ½A* ; B* ; C* ; D* n are strictly equivalent if and only if there is a constant square matrix T, with jTj 6¼ 0, such that the following relations hold: A ¼ TA* T1 B ¼ TB*
or
1
or 1
C ¼ C* T D ¼ D*
A* ¼ T1 AT
or or
ð3:9-1aÞ
B* ¼ T B
ð3:9-1bÞ
C* ¼ CT
ð3:9-1cÞ
D* ¼ D
ð3:9-1dÞ
The above definition originates from the following: consider the state transformation x ¼ Tx* ;
where jTj 6¼ 0
where x is the original state vector and x* is the new state vector. Substitute this transformation in the state equations of system ½A; B; C; Dn , i.e., of the system x ¼ Ax þ Bu y ¼ Cx þ Du Then, we have T x * ¼ ATx* þ Bu y ¼ CTx* þ Du Premuliplying the first equation by T1 yields the following state-space description x * ¼ T1 ATx* þ T1 Bu ¼ A* x* þ B* u y ¼ CTx* þ Du ¼ C* x* þ D* u where A* ¼ T1 AT, B* ¼ T1 B, C* ¼ CT, and D* ¼ D. These four relations are the relations stated in Eqs (3.9-1). The input–output description of a linear time-invariant system under strict equivalence is invariant. This property is proven in the following theorem. Theorem 3.9.1 If the linear time-invariant systems ½A; B; C; Dn and ½A* ; B* ; C* ; D* n are strictly equivalent, then these two systems have the same input–output description, i.e., their transfer function matrices are equal.
Mathematical Models of Systems
93
Proof Let HðsÞ ¼ CðsI AÞ1 B þ D and H* ðsÞ ¼ C* ðsI A* Þ1 B* þ D* be the transfer function matrices of systems ½A; B; C; Dn and ½A* ; B* ; C* ; Dn , respectively. By substituting the relations of strict equivalence (3.9-1) in HðsÞ, we have HðsÞ ¼ CðsI AÞ1 B þ D ¼ C* T1 ½sI TA* T1 1 TB* þ D* ¼ C* T1 ½TðsI A* ÞT1 1 TB* þ D* ¼ C* T1 T½sI A* 1 T1 TB* þ D* ¼ C* ½sI A* 1 B* þ D* ¼ H* ðsÞ
ð3:9-2Þ
All minimal dimension realizations of a linear time-invariant system are strictly equivalent among themselves. This property is given in the following theorem. Theorem 3.9.2 Let system ½A; B; C; Dn be a minimum dimension realization of the transfer function matrix HðsÞ. Then, any other minimum dimension realization ½A* ; B* ; C* ; D* n of HðsÞ is given by the relations A* ¼ T1 AT B* ¼ T1 B C* ¼ CT D* ¼ D
ð3:9-3Þ
where T is an arbitrary constant square n n matrix, with jTj 6¼ 0. From the above, it follows that the transition from state equations to the input–output description is only a computational problem, while the transition from the input–output description to the state equations, is first a theoretical and then a computational problem. Finally, from a practical point of view, the realization problem is essentially the selection of the most appropriate matrices A, B, C, and D among many equivalent descriptions in state space.
3.10
BLOCK DIAGRAMS
Block diagrams may be considered as a form of system description that provides a simplified overview schematic diagram of a system. Each block is characterized by an input–output description, by its input signal, and by its output signal (see Figure 3.13). The frequency domain appears to facilitate the use of block diagrams and it is for this reason that transfer functions are used to describe each block. When describing a system by block diagrams, what we actually have is many blocks appropriately linked to form the overall block diagram of the system. To handle complex block diagrams, one needs special rules that specify the significance of each connection and assist in simplifying or modifying the block diagram. The most important of such rules are listed below.
94
Chapter 3
Figure 3.13
3.10.1
Single block system.
Block Diagram Rules
1 Summation Point (Figure 3.14) The point where two or more signals may be added or subtracted is called a summation point. 2 Blocks in Cascade (Figure 3.15) Two or more blocks in cascade may be simplified to a single block in the following way. Going from left to right, we have the following relations for each block: X1 ðsÞ ¼ H1 ðsÞUðsÞ X2 ðsÞ ¼ H2 ðsÞX1 ðsÞ .. . Xn1 ðsÞ ¼ Hn1 ðsÞXn2 ðsÞ YðsÞ ¼ Hn ðsÞXn1 ðsÞ Eliminating X1 ðsÞ; X2 ðsÞ; . . . ; Xn1 ðsÞ in the above equations yields YðsÞ ¼ ½H1 ðsÞ H2 ðsÞ Hn ðsÞUðsÞ Hence, the transfer function HðsÞ of the equivalent single block diagram is HðsÞ ¼ H1 ðsÞH2 ðsÞ Hn ðsÞ
ð3:10-1Þ
3 Blocks in Parallel (Figure 3.16) Two or more blocks in parallel may be simplified to a single block in the following way. Going from top to bottom, the input–output relation for each block is
Figure 3.14
Summation point.
Mathematical Models of Systems
Figure 3.15
95
Block diagram with blocks in cascade. (a) Blocks in cascade; (b) equivalent
single block.
Figure 3.16 block.
Block diagram with blocks in parallel. (a) Blocks in parallel; (b) equivalent
96
Chapter 3
X1 ðsÞ ¼ H1 ðsÞUðsÞ X2 ðsÞ ¼ H2 ðsÞUðsÞ .. . Xn1 ðsÞ ¼ Hn1 ðsÞUðsÞ Xn ðsÞ ¼ Hn ðsÞUðsÞ Eliminating X1 ðsÞ; X2 ðsÞ; . . . ; Xn ðsÞ in the above equation yields YðsÞ ¼ ½H1 ðsÞ þ H2 ðsÞ þ þ Hn ðsÞUðsÞ Hence, the transfer function HðsÞ of the equivalent single block diagram is HðsÞ ¼ H1 ðsÞ þ H2 ðsÞ þ þ Hn ðsÞ
ð3:10-2Þ
4 Conversion of a Closed-Loop System to an Open-Loop System (Figure 3.17) The definitions of closed-loop and open-loop systems have been presented in Sec. 1.3. Consider the closed-loop system of Figure 3.17a. The transfer functions GðsÞ and FðsÞ are called the forward-path transfer function and the feedback-path transfer function, respectively. The closed-loop system of Figure 3.17a may be converted into an open-loop, single-block system as follows. From the closed-loop system, we have UðsÞ ¼ RðsÞ FðsÞYðsÞ YðsÞ ¼ GðsÞUðsÞ Eliminating UðsÞ gives GðsÞ YðsÞ ¼ RðsÞ 1 GðsÞFðsÞ
Figure 3.17 system.
Block diagrams of (a) a closed-loop system and (b) its equivalent open-loop
Mathematical Models of Systems
97
Hence, the transfer function HðsÞ of the equivalent open-loop system is HðsÞ ¼
GðsÞ 1 GðsÞFðsÞ
ð3:10-3Þ
Special Case If the feedback-path transfer function FðsÞ is unity, i.e., if FðsÞ ¼ 1, then Eq. (3.10-3) takes on the form GðsÞ HðsÞ ¼ ð3:10-4Þ 1 GðsÞ 5 Conversion of an Open-Loop System to a Closed-Loop System (Figure 3.18) An open-loop system can be converted into a closed-loop system in the following way: Solving Eq. (3.10-4) for GðsÞ gives GðsÞ ¼
HðsÞ 1 HðsÞ
ð3:10-5Þ
Hence, an open-loop system, having transfer function HðsÞ, is equivalent to a closedloop system with unity FðsÞ and with GðsÞ given by Eq. (3.10-5). 6 Conversion of the Feedback-Path Transfer Function F ðsÞ into Unity Consider the closed-loop system shown in Figure 3.17a. An equivalent block diagram with unity FðsÞ is the closed-loop system shown in Figure 3.19. From Figure 3.19 we have UðsÞ ¼
RðsÞ YðsÞ FðsÞ
and
YðsÞ ¼ GðsÞFðsÞUðsÞ
Eliminating UðsÞ gives
Figure 3.18 system.
Block diagrams of (a) an open-loop system and (b) its equivalent closed-loop
98
Chapter 3
Figure 3.19
HðsÞ ¼
Closed-loop system with unity feedback-path transfer function.
YðsÞ GðsÞ ¼ RðsÞ 1 GðsÞFðsÞ
Hence, both diagrams have the same transfer function, and thus they are equivalent. 7 Moving a Summation Point Consider the block diagram shown in Figure 3.20a. An equivalent block diagram with the summation point shifted is as shown in Figure 3.20b. The diagram shown in Figure 3.20a gives YðsÞ ¼ HðsÞU1 ðsÞ þ U2 ðsÞ while the diagram shown in Figure 3.20b gives U2 ðsÞ ¼ HðsÞU1 ðsÞ þ U2 ðsÞ YðsÞ ¼ HðsÞ U1 ðsÞ þ HðsÞ Hence, the two block diagrams are equivalent.
Figure 3.20
Moving a summation point. (a) Summation point following HðsÞ; (b) summation point proceeding HðsÞ.
Mathematical Models of Systems
99
8 Moving a Pickoff Point Consider the block diagram shown in Figure 3.21a. An equivalent block diagram is shown in Figure 3.21b. From the diagram shown in Figure 3.21a, we have that YðsÞ ¼ HðsÞUðsÞ; also, from the diagram shown in Figure 3.21b, we have that YðsÞ ¼ HðsÞUðsÞ. Hence, both block diagrams are equivalent. All above cases of equivalent block diagrams are frequently used. Figure 3.22 summarizes these cases, while other additional useful cases of equivalent block diagrams are included.
3.10.2 Simplification of Block Diagrams In the two examples that follow, we apply the rules of block diagrams of Subsec. 3.10.1 to simplify complex block diagrams. Example 3.10.1 Simplify the block diagram shown in Figure 3.23 to a single block. Solution This can be done step by step as follows. First, simplify the closed-loop system involving the blocks G1 ðsÞ, G2 ðsÞ, and F1 ðsÞ to an open-loop system, as in Figure 3.24, to yield G4 ðsÞ ¼
G1 ðsÞG2 ðsÞ 1 G1 ðsÞG2 ðsÞF1 ðsÞ
Figure 3.21 Moving a pickoff point. (a) Pickoff point at the input of HðsÞ; (b) pickoff point at the output of HðsÞ.
100
Figure 3.22
Chapter 3
Equivalent block diagrams.
Mathematical Models of Systems
Figure 3.22
(contd.)
101
102
Figure 3.22
Chapter 3
(contd.)
Mathematical Models of Systems
Figure 3.23
103
Original block diagram.
Then, simplify the two blocks in cascade, as shown in Figure 3.25, where G5 ðsÞ ¼ G4 ðsÞG3 ðsÞ. Finally, simplify the closed-loop system shown in Figure 3.25 to an open-loop system, as in Figure 3.26, to yield HðsÞ ¼
G5 ðsÞ 1 G5 ðsÞF2 ðsÞ
Example 3.10.2 Simplify the block diagram shown in Figure 3.27. Solution This diagram has two inputs, R1 ðsÞ and R2 ðsÞ. To determine the response YðsÞ we work as follows. Using the superposition principle, YðsÞ can be determined by adding two responses: the response Y1 ðsÞ due to the input R1 ðsÞ, where the other input is assumed zero, i.e., when R2 ðsÞ ¼ 0; the response Y2 ðsÞ due to the input R2 ðsÞ, where the other input is assumed zero, i.e., when R1 ðsÞ ¼ 0. Thus, when R2 ðsÞ ¼ 0, the block diagram will be as shown in Figure 3.28, and the response Y1 ðsÞ, due to the input R1 ðsÞ, will be G1 ðsÞG2 ðsÞ Y1 ðsÞ ¼ R ðsÞ 1 G1 ðsÞG2 ðsÞFðsÞ 1 Similarly, when R1 ðsÞ ¼ 0, the block diagram will be as shown in Figure 3.29, and the response Y2 ðsÞ, due to the input R2 ðsÞ, will be
Figure 3.24
First simplification of the block diagram of Figure 3.23.
104
Chapter 3
Figure 3.25
Second simplification of the block diagram of Figure 3.23.
Figure 3.26
Simplification of the block diagram of Figure 3.23 in a single block.
Figure 3.27
Block diagram with two inputs.
Figure 3.28
The block diagram of Figure 3.27 when R2 ðsÞ ¼ 0.
Figure 3.29
Block diagram of Figure 3.27 when R1 ðsÞ ¼ 0.
Mathematical Models of Systems
105
Y2 ðsÞ ¼
G2 ðsÞ R ðsÞ 1 G1 ðsÞG2 ðsÞFðsÞ 2
Hence, the total response YðsÞ of Figure 3.27 will be G1 ðsÞG2 ðsÞ G2 ðsÞ R1 ðsÞ þ R ðsÞ YðsÞ ¼ Y1 ðsÞ þ Y2 ðsÞ ¼ 1 G1 ðsÞG2 ðsÞFðsÞ 1 G1 ðsÞG2 ðsÞFðsÞ 2 Remark 3.10.1 Systems described in state space can also be represented by block diagrams. For example, consider the system (3.7-16a,b), i.e., the system ¼ AxðtÞ þ BuðtÞ xðtÞ yðtÞ ¼ CxðtÞ þ DuðtÞ The block diagram of this system is given in Figure 3.30, where the double lines represent multiple signals.
3.11
SIGNAL-FLOW GRAPHS
3.11.1 Definitions A signal-flow graph, as in the case of block diagrams, gives a simplified schematic overview of a system. In particular, a signal-flow graph gives this view based on the equations which describe the system. A signal-flow graph is composed of nodes (junctions) and branches. Each node represents a system’s variable. We have three types of nodes: . A source or input node (independent variable) is a node where no branch ends and from which one or more branches begin (Figure 3.31a). . A sink or output node (dependent variable) is a node to which one or more branches end and from which no branch begins (Figure 3.31b). . A mixed or generalized node is a node to which branches both end and begin (Figure 3.31c).
Figure 3.30
State equations block diagram.
106
Chapter 3
Figure 3.31
Nodes, direction, gain, and paths of signal-flow graphs.
Every branch connects two nodes and has two characteristics: the direction and the gain. Direction is simply the direction of the flow of the signal from one node to another (Figure 3.31d). Gain or transmittance or transfer function is the quantity Tij which relates the variables (nodes) xi and xj in the cause-and-effect sense. Hence, the direction and the gain are related by the equation xj ¼ Tij xi . If the direction of the branch was opposite, then the equation would be xi ¼ Tji xj . A signal-flow graph forms ‘‘paths.’’ Depending on the particular form of these paths, the following definitions are given: . A path is a succession of branches which have the same direction. For example, in Figure 3.31e, the combinations x1 x2 x3 , x1 x2 x3 x4 , and x3 x4 x5 x6 are paths. . A forward path is the path which starts from the input (source) node and ends at the otput (sink) node. For example, the path x1 x2 x3 x4 x5 x6 in Figure 3.31e is a forward path. . A loop is the path that begins and ends in the same node and along which no other node is encountered more than once. For example, the path x4 x5 x4 in Figure 3.31e is a loop. . A single-branch loop is a loop formed by a single branch. For example, the paths x2 x2 and x3 x3 in Figure 3.31e are single-branch loops.
3.11.2
Construction of Signal-Flow Graphs
Consider a system described by the following equations T11 x1 þ T12 x2 ¼ x3 T21 x1 þ T22 x2 ¼ x4
ð3:11-1Þ
Mathematical Models of Systems
Figure 3.32
107
Signal-flow graph of system in Eq. (3.11-1).
The signal-flow graph of this system is composed of the nodes x1 ; x2 ; x3 , and x4 , properly linked with the branch gains T11 ; T12 ; T21 , and T22 , so that the equations of the given system are satisfied. It can be easily shown that the diagram of Figure 3.32 is the signal-flow graph sought of Eq. (3.11-1). Similarly, for the system described by the equations x2 ¼ T12 x1 þ T32 x3 x3 ¼ T23 x2 x4 ¼ T34 x3
ð3:11-2Þ
one arrives at the signal-flow graph of Figure 3.33. Because a signal-flow graph is a schematic representation of the equations that describe the system, a good point to start for the construction of a signal-flow graph is to write down the equations of the system. This is done by applying the physical laws that govern the particular system. For example, in electrical networks one applies the Kirchhhoff’s laws of currents and voltages, while in mechanical systems one applies d’Alembert’s law of forces. With regard to the network equations, the following are pointed out. We consider as signals the node voltages and the loop currents. For a network with N passive elements (resistors, capacitors, and inductors) we can write Nk 1 node equations and N ðNk 1Þ loop equations, where Nk is the number of nodes. The total number of node and loop equations, and hence the total number of variables (or signals) for the network, is N. Example 3.11.1 Find the signal-flow graph for the network shown in Figure 3.34.
Figure 3.33
Signal-flow graph of system in Eq. (3.11-2).
108
Chapter 3
Figure 3.34
A resistive network of two loops.
Solution The network’s variables are v, v1 , v2 , i1 , and i2 . Variable v is known and is the input to the graph. Writing down the network equations, going from left to right, we have the following four equations: v v1 ; R1
i1 ¼
v1 ¼ R3 ði1 i2 Þ;
i2 ¼
v1 v2 ; R2
v2 ¼ i2 R4
The signal-flow graph of the network can be easily constructed and is given in Figure 3.35. 3.11.3
Mason’s Rule
Simplification rules, analogous to those for block diagrams presented in Subsec. 3.10.2, have been developed for signal-flow graphs, among which the most popular is Mason’s rule. Mason’s rule has the attractive characteristic in that it yields the input–output gain T (i.e., the gain from the input to the output node) of a signal-flow graph directly, without intermediate steps. Mason’s rule has the form T¼
k 1X T n¼1 n n
where 1.
Tn is the gain of every forward path.
Figure 3.35
The signal-flow graph of the network of Figure 3.34.
ð3:11-3Þ
Mathematical Models of Systems
2.
109
is the determinant of the signal-flow graph, which is given by ¼ 1 L1 þ L2 L3 þ
3. 4.
where a. L1 is the gain of every loop and L1 is the sum of the gains of all the loops of the graph. b. L2 is the product of the gains of two nontouching loops (two loops are nontouching when they don’t have common nodes). L2 is the sum of the product of the gains of all possible combinations of two nontouching loops. c. L3 is the product of the gains of three nontouching loops, and so on. n is the determinant of the signal-flow graph which remains when the path which produces Tn is removed. k is the number of the forward paths.
Example 3.11.2 Determine the transfer function (the gain) T between v and v2 of the flow diagram of Figure 3.35. Solution The gain T between v and v2 will be determined using Mason’s rule as follows: 1 Forward Path There is one forward path, as shown in Figure 3.36. The gain T1 of this path is T1 ¼ R3 R4 =R1 R2 . 2 Evaluation of the Determinant There are three loops, as shown in Figure 3.37. The gains of the first, second, and third loop are R3 =R1 , R3 =R2 , and R4 =R2 , respectively. Hence R3 R3 R4 L1 ¼ þ þ R1 R2 R2 There are two nontouching loops, namely the first and the third loop. Thus, L2 ¼ R3 R4 =R1 R2 . Therefore, R R R R R ¼ 1 L1 þ L2 ¼ 1 þ 3 þ 3 þ 4 þ 3 4 R1 R2 R2 R1 R2 ¼
R1 R2 þ R1 R3 þ R1 R4 þ R2 R3 þ R3 R4 R1 R2
Figure 3.36
Forward path of the signal-flow graph of Figure 3.35.
110
Chapter 3
Figure 3.37
Loops in the signal-flow graph of Figure 3.35.
3 Evaluation of 1 Since all branches touch the forward path, it follows that 1 ¼ 1. Hence T¼
3.12
T1 1 R3 R4 ¼ R1 R2 þ R1 R3 þ R1 R4 þ R2 R3 þ R3 R4
MATHEMATICAL MODELS FOR CONTROL SYSTEMS COMPONENTS
In this section we will derive one or more of the four mathematical models presented in Sections 3.4–3.7 to describe several components which are used in control systems. In particular, we will derive mathematical models for DC and AC motors, tachometers, error detectors, gears, hydraulic actuators, and pneumatic amplifiers. Also, the block diagram and signal-flow graphs of several of these components will be presented. In Sec. 3.13 that follows (as well as in many other places in the book) we present the mathematical descriptions of several practical control systems. 3.12.1
DC Motors
One component which is often used in control systems is the DC motor. There are several types of DC motors. We present here only the separately excited type, because its characteristics present several advantages over others, particularly with regard to linearity. Separately excited DC motors are distinguished in two categories: those that are controlled by the stator, which are usually called field-controlled motors; those that are controlled by the rotor, which are usually called armaturecontrolled motors. 1 Motors Controlled by the Stator A simple diagram of a DC motor controlled by its stator (i.e., by its field) is given in Figure 3.38. For simplicity, we make the following approximations: a. The rotor’s current ia ðtÞ is constant, i.e., ia ðtÞ ¼ Ia . b. The magnetic flux ’ðtÞ between the stator and the rotor is given by the linear relation ’ðtÞ ¼ Kf if ðtÞ
ð3:12-1Þ
where Kf is a constant and if ðtÞ is the stator’s current. c. The torque Tm ðtÞ that is developed by the motor is given by the relation
Mathematical Models of Systems
Figure 3.38
111
A DC motor controlled by the stator.
Tm ðtÞ ¼ Km Ia ’ðtÞ
ð3:12-2Þ
where Km is a constant. d. The Kirchhoff’s voltage law for the stator network is Lf
dif þ Rf if ¼ vf ðtÞ dt
ð3:12-3Þ
e. The rotor’s rotational motion is described by the differential equation Jm
d!m þ Bm !m ¼ Tm ðtÞ; dt
!m ¼
dm dt
ð3:12-4Þ
where Jm is the torque inertia, Bm is the coefficient of friction, m ðtÞ is the angular position or displacement, and !m ðtÞ is the angular velocity of the motor. Substituting Eq. (3.12-1) into (3.12-2), we have Tm ðtÞ ¼ Km Kf Ia if ðtÞ
ð3:12-5Þ
Also, if we substitute Eq. (3.12-5) into Eq. (3.12-4), we have Jm
d!m þ Bm !m ¼ Km Kf Ia if ðtÞ dt
ð3:12-6Þ
In the sequel, we will present the four different mathematical models which describe the motor, together with the motor’s block diagram and signal-flow graph. a. Differential Equations These are the Eqs (3.12-5) and (3.12-4) i.e., the equations Lf
dif þ Rf if ¼ vf ðtÞ dt
ð3:12-7aÞ
Jm
d!m þ Bm !m ¼ Tm ðtÞ dt
ð3:12-7bÞ
b. State Equations Let x1 ¼ m , x2 ¼ !m , and x3 ¼ if be the state variables. Then, the system of differential equations (3.12-7), together with the equation x_ 1 ¼ x2 ¼ !m , take on the form
112
Chapter 3
x ¼ Ax þ bu where
2
3 m x ¼ 4 !m 5; if
and 2
0 A ¼ 40 0
ð3:12-8aÞ
y ¼ cT x
1 Tm1 0
3 0 1 Jm Km Kf Ia 5; Tf1
2
3 0 b ¼ 4 0 5; L1 f
2 3 1 c ¼ 405 0 ð3:12-8bÞ
and where u ¼ vf , Tm ¼ Jm =Bm is the mechanical time constant, and Tf ¼ Lf =Rf is the electrical time constant of the stator. c. Transfer Function Applying the Laplace transform to Eqs (3.12-7) or (3.12-8) and after some algebraic manipulations we derive the following transfer function GðsÞ ¼
1 m ðsÞ K m K f Ia B1 m Rf Km Kf Ia ¼ ¼ Vf ðsÞ sðJm s þ Bm ÞðLf s þ Rf Þ sðTm s þ 1ÞðTf s þ 1Þ
ð3:12-9Þ
where all initial conditions of the motor are assumed zero. d. Block Diagram A simple block diagram of the motor is given in Figure 3.39a. e. Signal-Flow Graph The signal-flow graph of the state equations (3.12-8) is given in Figure 3.39b. 2 Motors Controlled by the Rotor A simple diagram of a DC motor controlled by its rotor (i.e., by its armature) is given in Figure 3.40. For simplicity, we assume the following approximations: a. The stator current if ðtÞ is constant, i.e., if ðtÞ ¼ If . b. The magnetic flux ’ðtÞ, given by Eq. (3.12-1), will be constant since if ðtÞ is constant, i.e., ’ðtÞ ¼ Kf If ¼ . c. The torque Tm ðtÞ, given by Eq. (3.12-2), now has the form Tm ðtÞ ¼ Km ia ðtÞ ¼ Ki ia ðtÞ
ð3:12-10Þ
where Ki ¼ Km . d. The voltage vb ðtÞ is proportional to the angular velocity of the motor, i.e., vb ðtÞ ¼ Kb !m ðtÞ e. The Kirchhoff’s law of voltages for the rotor network is La
dia þ Ra ia þ vb ¼ va dt
Figure 3.39a
Block diagram of a motor controlled by the stator.
ð3:12-11Þ
Mathematical Models of Systems
Figure 3.39b
Figure 3.40
113
Signal-flow graph of a motor controlled by the stator.
A DC motor controlled by the rotor.
or La
dia þ Ra ia þ Kb !m ¼ va dt
ð3:12-12Þ
where use was made of Eq. (3.12-11). f. The rotor’s rotational motion is described by the differential equation Jm
d!m þ Bm !m ¼ Tm ðtÞ ¼ Ki ia ; dt
!m ¼
dm dt
ð3:12-13Þ
In what follows, we present the four mathematical models and the block diagram and signal-flow graph of the motor. a. Differential Equations These are the Eqs (3.12-12) and (3.12-13), i.e., the equations dia þ Ra ia þ Kb !m ¼ va dt d! Jm m þ Bm !m ¼ Ki ia dt La
ð3:12-14aÞ ð3:12-14bÞ
b. State Equations Let x1 ¼ m , x2 ¼ !m , and x3 ¼ ia be the state variables. Then the system of differential equations (3.12-14), together with the equation x_ 1 ¼ x2 ¼ !m , take on the form
114
Chapter 3
x ¼ Ax þ bu where
2
3 m x ¼ 4 !m 5; ia
and 2
0 A ¼ 40 0
ð3:12-15aÞ
y ¼ cT x
1 Tm1 L1 a Kb
0
3
1 Jm Ki 5; Ta1
2
3 0 b ¼ 4 0 5; L1 a
2 3 1 c ¼ 405 0 ð3:12-15bÞ
and where u ¼ va , and Ta ¼ La =Ra is the electrical time constant of the rotor. c. Transfer Function Apply the Laplace transform to Eq. (3.12-14) to yield sLa Ia ðsÞ þ Ra Ia ðsÞ þ Kb m ðsÞ ¼ Va ðsÞ
ð3:12-16aÞ
Jm sm ðsÞ þ Bm m ðsÞ ¼ Ki Ia ðsÞ;
ð3:12-16bÞ
m ðsÞ ¼ sm ðsÞ
where all initial conditions are assumed zero. The transfer function will then be m ðsÞ Ki ¼ Va ðsÞ sðsLa þ Ra ÞðJm s þ Bm Þ þ Ki Kb s Ki ¼ La Jm s3 þ ðRa Jm þ La Bm Þs2 þ ðRa Bm þ Ki Kb Þs
GðsÞ ¼
ð3:12-17Þ
d. Block Diagram The equations of system (3.12-16) can be represented by the closed-loop block diagram of Figure 3.41. From this diagram it is clear that the feedback signal Kb sm ðsÞ is actually the derivative of the output m ðtÞ. For this reason this type of control is called ‘‘derivative’’ or ‘‘speed’’ feedback control. e. Signal-Flow Graph The signal-flow graph of the state equations (3.12-15) is given in Figure 3.42. 3.12.2
AC Motors
In cases where the control requires low power, then two-phase alternating current (AC) motors are usually used. A two-phase AC motor has two coils on the stator which are perpendicular to each other (Figure 3.43). One of the two coils is used as a reference, while the other is used to control the rotor. The transfer function, which
Figure 3.41
Block diagram of a motor controlled by the rotor.
Mathematical Models of Systems
Figure 3.42
115
Signal-flow graph of a motor controlled by the rotor.
relates the angular position of the rotor m ðsÞ to the control voltage V2 ðsÞ, is given approximately, by the following relation GðsÞ ¼
m ðsÞ K ¼ V2 ðsÞ sðTs þ 1Þ
ð3:12-18Þ
where K and T are the gain and time constant of the motor, respectively. 3.12.3 Tachometers The tachometer has the property that its output voltage vo ðtÞ is proportional to the angular velocity !ðtÞ of the motor, i.e., vo ðtÞ ¼ Kt !ðtÞ where Kt is the constant of the tachometer.
Figure 3.43
Two-phase AC motor.
ð3:12-19Þ
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Chapter 3
3.12.4
Error Detectors
Error detectors are special components whose output is the difference of two signals. Such components are very useful in control systems. Two rather well-known types of error detectors are presented below. 1 Operational Amplifier with Resistors This error detector is given in Figure 3.44. The output voltage vo ðtÞ of the amplifier is vo ðtÞ ¼
Rf R v ðtÞ f v2 ðtÞ R1 1 R2
ð3:12-20Þ
Hence, for R1 ¼ R2 ¼ Rf , the voltage vo ðtÞ will be vo ðtÞ ¼ v1 ðtÞ v2 ðtÞ
ð3:12-21Þ
2 The Potentiometer This error detector is given in Figure 3.45. The voltage V0 is constant. Depending on the position of the points A and B on the resistors, the voltages v1 ðtÞ and v2 ðtÞ are produced, respectively. The voltage eðtÞ across the points A and B will then be eðtÞ ¼ Kp ½v1 ðtÞ v2 ðtÞ
ð3:12-22Þ
where eðtÞ is the error voltage and Kp is the potentiometer constant, which depends on the sensitivity of the elements involved. 3 Synchrosystems (Figure 3.46) A synchrosystem is usually composed of two parts—the transmitter and the receiver. Both the transmitter and the receiver are actually three-phase rotating electrical motors. Let t and r be the angles of the axis of rotation of the transmitter rotor and of the receiver rotor, respectively. It can be shown that if vi ðtÞ ¼ sin !t is the synchrosystem input voltage, then the amplitude of the output voltage vo ðtÞ of the synchrosystem will be proportional to the difference of the angles t and r , i.e., vo ðtÞ ¼ Kðt r Þ sin !t
Figure 3.44
Error detector using operational amplifiers and resistors.
ð3:12-23Þ
Mathematical Models of Systems
Figure 3.45
117
Error detector using potentiometers.
where K is the synchrosystem’s constant. Relation (3.12-23) suggests that a synchrosystem can be used to measure the difference between t and r . 3.12.5 Gears Gears are used in order to increase or decrease the rotational speed of a motor. Figure 3.47 shows a simple arrangement of gears where N1 ; N2 R1 ; R2 T1 ; T2 1 ; 2 !1 ; !2 J1 ; J2 B1 ; B2 C1 ; C2
Figure 3.46
¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼
number of teeth of each gear radius of each gear torque of each gear angular displacement of each gear angular velocity of each gear torque of inertia of each gear viscous friction coefficient of each gear Coulomb friction coefficient of each gear
A synchrosystem layout.
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Chapter 3
Figure 3.47
(a) Ideal and (b) nonideal gears.
For the ideal system of coupled gears of Figure 3.47a, where the torques of inertia J1 and J2 and the friction coefficients B1 , B2 , C1 , and C2 are considered to be zero, the following relations hold R1 N2 ¼ R2 N1
ð3:12-24aÞ
R1 1 ¼ R2 2
ð3:12-24bÞ
T1 1 ¼ T2 2
ð3:12-24cÞ
Relation (3.12-24a) expresses the fact that the ratio of the number of teeth is proportional to the radii ratio. Relation (3.12-24b) expresses the fact that the distances run by the two gears are equal. Finally, relation (3.12-24c) expresses the fact that, given that there are no losses, the work done by one gear is equal to the work done by the other. From the above relations, we have that T1 2 N1 !2 R1 ¼ ¼ ¼ ¼ T2 1 N2 !1 R2
ð3:12-25Þ
It is well known that, in practice, the torques of inertia J1 and J2 , as well as the friction coefficients B1 , B2 , C1 , and C2 , are not zero. In this case, the algebraic relation (3.12-24c) is not valid, due to losses. Instead, the following dynamic relations hold: J1
d2 1 d ! þ B1 1 þ C1 1 þ T1 ¼ T 2 dt j! dt 1j
ð3:12-26Þ
J2
d2 2 d ! þ B2 2 þ C2 2 ¼ T2 dt j!2 j dt2
ð3:12-27Þ
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119
where T is the external torque applied. Relation (3.12-26) is the torque equation of the gear with N1 number of teeth, while relation (3.12-27) is the torque equation of the gear with N2 number of teeth. From relation (3.12-25), we have that T2 ¼ N 1 T1 and 2 ¼ N1 , where N ¼ N1 =N2 . Substitute these relations in Eq. (3.12-27) to yield NJ2
d2 1 d ! þ NB2 1 þ C2 2 ¼ N 1 T1 dt j!2 j dt2
or N 2 J2
d2 1 d ! þ N 2 B2 1 þ NC2 2 ¼ T1 2 dt j!2 j dt
ð3:12-28Þ
If we substitute Eq. (3.12-28) into Eq. (3.12-26), we have J1
d2 1 d þ B 1 1 þ Tc ¼ T dt dt2
ð3:12-29Þ
where J1 ¼ J1 þ N 2 J2 ;
B 1 ¼ B1 þ N 2 B2 ;
and
Tc ¼ C1
!1 ! þ NC2 2 j!1 j j!2 j
Relation (3.12-29) is the differential equation which describes the dynamic behavior of the gears with reference to the gear with N1 number of teeth. It is noted that the above analysis of gears is analogous to that of transformers. Indeed, gears and transformers are analogous systems, where the primary transformer coil corresponds to the gear with N1 number of teeth and the secondary transformer coil corresponds to the gear with N2 number of teeth. 3.12.6 Hydraulic Actuator or Servomotor The hydraulic actuator or servomotor is the main component in any hydraulic control system. A typical hydraulic actuator is given in Figure 3.48 and operates as follows: let the actuator input be the position x of the piston valve and the actuator output be the position y of the power cylinder piston. The actuator excitation is caused by pressing the piston valve, which results in moving the valve to the right or to the left from its equilibrium position by x. Then the power cylinder piston’s position will move correspondingly to the right or to the left from the equilibrium position by y. The displacement y results as follows: as the piston valve moves to the right, oil will run towards port 1. As this oil is under pressure P1 , the power cylinder piston will move to the right by y, because the oil pressure P2 in port 2 is less than the pressure P1 . Let q be the oil flow per minute. Then, a first approximation, gives q ¼ K1 x K2 p;
p ¼ P2 P1
ð3:12-30Þ
where K1 and K2 are constants. Also, the following relation is valid: A y ¼ qt
ð3:12-31Þ
where A is the area of the power cylinder piston surface and is the oil density. If we eliminate q from relations (3.12-30) and (3.12-31), then we arrive at the relation
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Chapter 3
Figure 3.48
The hydraulic actuator.
1 y p ¼ 2 K1 x A t K The force f produced by the power cylinder piston will be A y f ¼ Ap ¼ K x A K2 1 t
ð3:12-32Þ
ð3:12-33Þ
Finally, the differential equation that describes the motion of the load (mass) m will be m
d2 y dy AK1 A2 dy þB ¼f ¼ u 2 dt K2 dt K2 dt
or " # d2 y A2 dy AK1 m 2 þ Bþ ¼ u K2 dt K2 dt where we have set y=t ¼ dy=dt and x ¼ u. The transfer function of the hydraulic actuator will then be
ð3:12-34Þ
Mathematical Models of Systems
GðsÞ ¼
121
YðsÞ K ¼ UðsÞ sðTs þ 1Þ
ð3:12-35Þ
where K¼
BK2 A 1 þ AK1 K1
and
T¼
mK2 BK2 þ A2
If we assume that T ’ 0, then GðsÞ ¼ K=s. Therefore, we observe that the hydraulic actuator of Figure 3.48 serves simultaneously as an amplifier and as an integrator. 3.12.7 Pneumatic Amplifier A typical pneumatic amplifier is given in Figure 3.49a. Let the system input be the distance x between the nozzle and the flapper, and the system output be the pressure P which regulates a control valve. Figure 3.49b shows the relation between x and P, where Ps is the pressure of the forced air and Pa is the atmospheric pressure. This system operates as an amplifier, because the power which is required to move the flapper by a distance x is much less than the power P delivered by the system to the control valve. A practical application of pneumatic amplifiers is in the liquid-level control system given in Figure 1.12. 3.13
MATHEMATICAL MODELS OF PRACTICAL CONTROL SYSTEMS
In this section, the mathematical models of several practical closed-loop control systems are presented. These systems are used to control a specific variable such as voltage, position or speed of a mass, flow or level of a liquid, room temperature, satellite orientation control, etc. Such practical control systems are often used in process industries, as for example in the paper, cement, and food industries, etc.; in controlling airplanes, ships, spacecrafts, etc., and in high-precision control systems such as guns, rockets, radars, radio telescopes, satellites, and others.
Figure 3.49 (a) Pneumatic amplifier and (b) the effect of distance x upon the pressure P acting upon the control valve.
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3.13.1
Chapter 3
Voltage Control System
A voltage control system is given in Figure 3.50a. This system is designed so that the load voltage vL remains constant despite any changes in the load RL . It operates as follows: The reference voltage vr and the feedback voltage vb are constantly compared. Under ideal load conditions, i.e., when RL ! 1, the voltage error ve ¼ vr vb is such that the generator produces the desired voltage vL . Under nonideal load condtions, the resistance of RL decreases and the voltage vb also decreases, resulting in an increase in the error voltage ve . Increasing the error voltage ve increases the output voltage vf of the amplifier, which in turn increases the field current if . Further, increasing the current if , increases the generator voltage vg . Finally, increasing the voltage vg increases the voltage vL . As a result, the voltage vL will approach
Figure 3.50
Voltage control system. (a) Overall picture of the voltage control system; (b) schematic diagram of the system; (c) block diagram of the system.
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123
its desired initial value, i.e., its value when RL ! 1. This procedure is shown in the schematic diagram of Figure 3.50b. The differential equations that describe the voltage control system are ve ¼ vr vb
ð3:13-1aÞ
vf ¼ Ka ve
ð3:13-1bÞ
L
dif þ Rf if ¼ vf dt
ð3:13-1cÞ
vg ¼ Kg !if
ð3:13-1dÞ
ðRg þ RL Þia ¼ vg
ð3:13-1eÞ
R2 v R1 þ R2 L
ð3:13-1fÞ
vb ¼
The above relations refer (from top to bottom) to the error, the amplifier, the field of excitation, the generator’s voltage (see subsec. 3.12.1), the generator–load network, and the feedback voltage. In relation (3.13-1e), we have assumed that R1 þ R1 RL . The block diagram of the system is given in Figure 3.50c. The forward-path transfer function GðsÞ will be K RL Rf ; where K ¼ GðsÞ ¼ K K ! and Tf ¼ Lf =Rf ð3:13-2Þ sTf þ 1 Rg þ RL a g The transfer function HðsÞ of the closed-loop system is HðsÞ ¼
GðsÞ K K1 ¼ ¼ 1 þ Kp GðsÞ sTf þ 1 þ Kp K sT þ 1
ð3:13-3Þ
where Kp ¼
R2 ; R1 þ R2
T¼
Tf ; 1 þ Kp K
and
K1 ¼
K 1 þ Kp K
3.13.2 Position (or Servomechanism) Control System A well-known position (or servomechanism) control system is given in Figure 3.51a. This system is designed to control the load angular position. Specifically, the system must be such that any change in the command, i.e., in the input angular position r , is followed as closely as possible by the output angular position y . This is accomplished as follows: the command r is driven manually, rotating the slider of the input potentiometer by r degrees. The input angle r , as well as the output angle y , are converted into electrical signals, which subsequently are subtracted from each other to yield their difference e , which is called the error. The error e is driven into the amplifier. The amplifier output is then used to excite the armature-controlled motor (Subsec. 3.12.1). Finally, the motor rotates the load through the gears. Clearly, if e ¼ 0, then r ¼ y , meaning that the system has come to a standstill. In the case where e 6¼ 0, the motor will turn clockwise or counterclockwise, depend-
124
Chapter 3
Figure 3.51
Position (or servomechanism) control system. (a) Overall picture of a position control system; (b) schematic diagram of the system; (c) block diagram of the system.
ing on the sign of the error e , until e ¼ 0. This procedure is depicted in the schematic diagram in Figure 3.51b. It is remarked that the ideal situation would be, as soon as we apply the command input r , the load rotates instantly and stops at the angle y ¼ r . But, due to the inertia and other properties of the system, the output y cannot become r instantly (it has the general form shown in Figure 4.2). The algebraic and differential equations describing each subsystem, going from the input to the output, are 1 The Error Detector According to relation (3.12.22) we have e ðtÞ ¼ Kp ½r ðtÞ y ðtÞ where Kp is the potentiometer detector constant.
ð3:13-4Þ
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125
2 The DC Amplifier We have va ðtÞ ¼ Ka e ðtÞ
ð3:13-5Þ
where Ka is the amplifier gain constant. 3 The Motor This is an armature-controlled motor with a fixed field current whose dynamic equations are given in Subsec. 3.12.1. To the motor we have added gears plus the load. Hence, the motor equations are modified as follows: dia þ Ra ia þ vb ¼ va ; dt d!m þ B m !m ¼ Tm ; Jm dt
La
where vb ðtÞ ¼ Kb !m ðtÞ
ð3:13-6Þ
where Tm ðtÞ ¼ Ki ia ðtÞ
ð3:13-7Þ
¼ Jm þ N 2 JL , and B m ¼ Bm þ N 2 BL . The constants Jm and where N ¼ N1 =N2 ; Jm Bm are determined using the results of Subsec. 3.12.5, under the assumption that the gears are ideal, i.e., that there are no losses. JL is the moment of inertia and BL the viscosity coefficient of the load. Likewise, Jm and Bm are the moment of inertia and and B m of the subsystem, consisting of the viscosity coefficient of the motor and Jm the motor, the gears, and the load.
4 The Output We have dm ¼ !m dt
and
y ¼ Nm
ð3:13-8Þ
The forward-path transfer function is given by GðsÞ ¼
y ðsÞ Ka Ki N ¼ s þ B m Þ þ Ki Kb s e ðsÞ sðsLa þ Ra ÞðJm
ð3:13-9Þ
The above relation is essentially relation (3.12-17), where we have set y ðsÞ ¼ Nm ðsÞ and B m . The transfer function and the constants Jm and Bm have been replaced by Jm of the closed-loop system (see Figure 3.51c) will be y ðsÞ Kp Ka Ki N GðsÞ HðsÞ ¼ ¼ Kp ¼ r ðsÞ sðsLa þ Ra ÞðsJm 1 þ Kp GðsÞ þ B m Þ þ Ki Kb s þ Kp Ka Ki N ð3:13-10Þ Assuming that La ’ 0, then the forward-path transfer function GðsÞ reduces to GðsÞ ¼
Ka Ki N K ¼ s½Ra Jm s þ Ra B m þ Ki Kb sðAs þ BÞ
ð3:13-11Þ
where K¼
Ka Ki N ; Ra
A ¼ Jm ;
and
B ¼ B m þ
Ki Kb Ra
ð3:13-12Þ
Relation (3.13-11) shows that for La ’ 0 the open-loop system reduces to a second-order system.
126
3.13.3
Chapter 3
Speed Control System
A well-known speed control system is given in Figure 3.52a. This system is designed to control the angular velocity (speed) !y of the load, despite changes in the load. For this purpose the Ward–Leonard generator–motor layout is used. The command input vr is constantly compared with the output of the tachometer vy . The resulting error ve is driven into the amplifier, whose output is the voltage vf . Note that the input to the Ward–Leonard layout is the voltage vf , which excites the field of the generator G, and that the output is the angular velocity !m of the motor M. When the error ve ¼ 0, then the angular velocity !y of the load is the desired one. If ve 6¼ 0, the motor angular velocity !m will speed up or slow down, so that ve reaches zero, in which case !y reaches the desired value. It is important to point out that the closed-loop system involves speed feedback action via the tachometer.
Figure 3.52
Speed control system. (a) Overall picture of a speed control system; (b) schematic diagram of the system; (c) block diagram of the system.
Mathematical Models of Systems
127
The equations of the Ward–Leonard layout are as follows. The Kirchhoff’s law of voltages of the excitation field of the generator G is vf ¼ Rf if þ Lf
dif dt
ð3:13-13Þ
The voltage vg of the generator G is proportional to the current if , i.e., vg ¼ Kg if The voltage vm of the motor M is proportional to the angular velocity !m , i.e., vm ¼ Kb !m The differential equation for the current ia is Ra ia þ La
dia ¼ vg vm ¼ Kg if Kb !m dt
ð3:13-14Þ
where Ra ¼ Rag þ Ram and La ¼ Lag þ Lam . The torque Tm of the motor is proportional to the current ia , i.e., Tm ¼ Km ia The rotational motion of the rotor is described by Jm
d!m þ B m !m ¼ Km ia dt
ð3:13-15Þ
¼ Jm þ N 2 JL and B m ¼ Bm þ N2 BL , where N ¼ N1 =N2 . Here, Jm is the where Jm moment of inertia and Bm the viscosity coefficient of the motor: likewise, for JL and BL of the load. From the above relations, we can determine the transfer function of the Ward–Leonard (WL) layout (including the load):
GWL ðsÞ ¼
Kg Km N y ðsÞ ¼ s þ B m Þ þ Km Kb Vf ðsÞ ðLf s þ Rf Þ½ðLa s þ Ra ÞðJm
ð3:13-16Þ
where use was made of the relation !y ¼ N!m . The forward-path transfer function GðsÞ is given by GðsÞ ¼
Ka Kg Km N s þ B Þ þ K K ðLf s þ Rf Þ½ðLa s þ Ra ÞðJm m m b
where Ka is the gain constant of the amplifier. The tachometer (see Subsec. 3.12.3) is mechanically linked to the motor. The tachmotor equation is the following: vy ¼ Kt !y Finally, the transfer function of the closed-loop system (see Figure 3.52c) is the following third-order system: HðsÞ ¼
Ka Kg Km N y ðsÞ ¼ s þ B Þ þ K K þ K K K K N Vr ðsÞ ðLf s þ Rf Þ½ðLa s þ Ra ÞðJm m b a t g m m
If we assume that La ’ 0, then HðsÞ becomes the following second-order system: HðsÞ ¼
ðLf s þ
s Rf ÞðRa Jm
Ka Kg Km N þ Ra B m þ Km Kb Þ þ Ka Kt Kg Km N
ð3:13-17Þ
128
3.13.4
Chapter 3
Liquid-Level Control System
A simple liquid-level control system is given in Figure 3.53a. This system is designed such that the liquid level (i.e., the height y) remains constant despite the changes in the outflow rate qo of the liquid. It operates as follows. The load valve regulates the outflow rate qo of the liquid from the container and the control valve regulates the inflow rate qi of the liquid in the container. The liquid level y is monitored by the analog measuring device. The output of this device is connected to the control valve in order to control the inflow rate qi . When qo increases (decreases), then y decreases (increases) and the analog device opens (closes) the control valve, thus increasing (decreasing) the inflow rate qi . This procedure keeps the height y of the liquid level constant.
Figure 3.53
Liquid-level control system. (a) Overall picture of a liquid-level control system; (b) block diagram of the liquid-level analog measuring device; (c) block diagram of the closedloop system.
Mathematical Models of Systems
129
The differential equation describing the system is determined as follows. The continuity law of fluids states that the amount of liquid which enters the container minus the amount of liquid which leaves the container is equal to the increase of the amount of liquid in the container. Let the load valve be closed (which means that qo ¼ 0). Then, the continuity law of fluids gives qi dt ¼ Ady
ð3:13-18Þ
where qi is the volume of the liquid inflow per unit time, A is the horizontal area of the container, and dt and dy are the differentials of time and height, respectively. If the load valve is open (which means that qo 6¼ 0), then the continuity law of fluids gives ðqi qo Þ dt ¼ Ady
ð3:13-19Þ
Let the liquid flow be laminar. A laminar fluid system is characterized by the constants R (resistance of a valve or of a pipe) and C (capacitance of the container), which are defined as follows R ¼ y=qo
and
C¼A
ð3:13-20Þ
Substitute relation (3.13-20) into Eq. (3.13-19) to yield RC
dy þ y ¼ Rqi dt
ð3:13-21Þ
The differential equation (3.13-21) describes the open-loop system with input as the supply qi and output as the height y. The transfer function of the open-loop system will then be GðsÞ ¼
YðsÞ R ¼ Qi ðsÞ RCs þ 1
ð3:13-22Þ
The liquid-level analog measuring device operates as shown in Figure 3.53b. The height YðsÞ of the liquid level is compared with the reference signal RðsÞ producing the error EðsÞ ¼ RðsÞ Kf YðsÞ, where Kf is the constant of the measuring device. The error EðsÞ is driven into a pneumatic amplifier (see Subsec. 3.12.7) whose output is air with pressure PðsÞ ¼ Ka EðsÞ, where Ka is the amplification constant of the liquid-level measuring device. This pressure is in turn driven into the control valve. This results in the liquid inflow rate Qi ðsÞ delivered to the tank, where Qi ðsÞ ¼ Kv PðsÞ, where Kv is the control valve constant. Hence, the overall block diagram of the closed-loop system will be as shown in Figure 3.53c. The transfer function of the closed-loop system is given by HðsÞ ¼
YðsÞ Ka Kv R ¼ RðsÞ RCs þ 1 þ Kf Ka Kv R
ð3:13-23Þ
3.13.5 Temperature Control System A simple room temperature control system is given in Figure 3.54. This system is designed such that the room temperature remains constant despite changes in the external temperature. The operation of the system is regulated via a temperature analog measurement device. This device compares the room temperature y with the desired temperature r and transforms the error signal e (i.e., the difference r y Þ
130
Chapter 3
Figure 3.54
Room temperature control system. (a) Overall picture of a room temperature control system; (b) block diagram of the temperature analog measuring device; (c) block diagram of the closed-loop system.
into a pressure signal. This pressure acts on the control valve, which accordingly closes or opens, thus controlling the supply of the liquid fuel. The amount of the liquid fuel which enters the burner, essentially controls the temperature i of the hot air. This procedure results in maintaining the room temperature y constant. We define the following parameters of the system: . C ¼ heat capacity of the room’s air. If the room’s air has mass m and specific heat capacity , then C ¼ m.
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131
. R ¼ thermal resistance of the room’s air. If A is the horizontal area of the room, h is the height, and k is the thermal conductance coefficient of the air, then we have the approximate relation R ¼ h=kA. . q ¼ flow of the air mass through the room per unit time. If i and y are the input and output temperatures of the room, respectively, then
q ¼ ði y Þ=R
ð3:13-24Þ
The mathematical model describing the system is determined as follows. Let the room temperature be y . Then, an increase dy in the temperature y requires Cdy amount of heat. This amount of heat must be delivered by the hot air flow q in a time dt. Hence, we have Cdy ¼ qdt ¼
i y dt R
or
RC
dy þ y ¼ i dt
ð3:13-25Þ
Relation (3.13-25) describes an open-loop system with input as the temperature of the hot air and output as the temperature y of the room. The transfer function of this open-loop system is given by GðsÞ ¼
y ðsÞ 1 ¼ i ðsÞ RCs þ 1
ð3:13-26Þ
The temperature analog measuring device operates as shown in Figure 3.54b. The block diagram of the closed-loop system is given in Figure 3.54c. Let FðsÞ ¼ Lf f ðtÞg be the flow of the liquid fuel, while Kd is a constant that relates the liquid fuel flow f ðtÞ to the temperature i ðtÞ of the hot air produced. The transfer function of the closed-loop system is HðsÞ ¼
y ðsÞ Ka Kv Kd ¼ r ðsÞ RCs þ 1 þ Ka Kv Kd
ð3:13-27Þ
where Kv is the valve constant. 3.13.6 Chemical Composition Control System A simple chemical composition control system is given in Figure 3.55a. This system is designed such that the chemical composition of a certain product, which comes for example from mixing water and a certain liquid, remains the same despite any changes in operating conditions, e.g., in temperature or pressure. The chemical composition of the mixture is compared with the desired composition. The resulting error is converted into pressure, which controls the valve. If the mixture content is smaller than the desired content, then the valve opens, allowing more liquid to enter the container. If the mixture content is greater than the desired content, then the valve closes so that less liquid enters the container. This procedure guarantees that the chemical composition of the product remains constant. A simplified mathematical model which describes the operation of the chemical composition control system is determined as follows. Let qv , q , and qy be the flows (m3 /sec) of water, liquid, and of produced mixture, where the flow qv is maintained constant. Also, let x and xy be the contents (kg/m3 ) of the liquid and of the produced mixture in a certain chemical substance, e.g., calcium. Then, in a time
132
Chapter 3
Figure 3.55
Chemical composition control system. (a) Overall picture of a chemical composition control system; (b) block diagram of the chemical composition analog measuring device and sample analysis apparatus; (c) block diagram of the closed-loop system.
interval dt, q x dt mass of calcium enters the container and qy xy dt mass of calcium leaves the container. Hence, the change dxy in the mixture content in calcium will be dxy ¼
q x dt qy xy dt V
ð3:13-28Þ
where V is the volume of the container. According to the law of mass preservation we have
Mathematical Models of Systems
133
qy ¼ qv þ q
ð3:13-29Þ
Since qv q , we have that qy ’ qv ¼ K ¼ constant. Hence Eq. (3.13-28) becomes dxy K x þ xy ¼ q dt V V
ð3:13-30Þ
Relation (3.13-30) describes the open-loop system with input q and output xy . The transfer function of the open-loop system is GðsÞ ¼
Xy ðsÞ x ¼ Q ðsÞ Vs þ K
ð3:13-31Þ
In Figure 3.55b the block diagram of the chemical composition analog measuring device and the sample analysis apparatus are given, where Kf and Ka are the constants of this layout and Xr ðsÞ is the reference signal. From Figure 3.55c we find that the transfer function of the closed-loop system is given by HðsÞ ¼
Xy ðsÞ K ¼ Xr ðsÞ Ts þ 1
ð3:13-32Þ
where K¼
Ka Kv x K þ Kf Ka Kv x
and
T¼
V K þ Kf Ka Kv x
3.13.7 Satellite Orientation Control System This system (see Figure 3.56a) is designed so that the satellite’s telescope, which is mounted on the satellite, is constantly in perfect orientation with a star, despite disturbances such as collisions with meteorites, gravity forces, etc. The system operates as follows: the orientation of the telescope is achieved with the help of a reference star, which is much brighter than the star under observation. It is initially assumed that the axis of the telescope is in line with the star under observation. At this position, the axis forms an angle of r with the line which connects the telescope to the reference star. The angle r is known in advance and is the system’s excitation. If for any reason the angle y , which is formed by the axis of the telescope with the reference star, is different from the desired angle r , then the control system must align the Z-axis so that y ¼ r . The block diagram of the closed-loop system is given in Figure 3.56b, where the block designated ‘‘thruster’’ is excited by the controller’s output and produces torque forces about the X-axis. These forces will ultimately cause the correction in the angle y , so that y ¼ r . The various blocks in Figure 3.56c are described, respectively, by the relations EðsÞ ¼ Kt ½r ðsÞ y ðsÞ
ð3:13-33aÞ
VðsÞ ¼ Gc ðsÞEðsÞ
ð3:13-33bÞ
TðsÞ ¼ Gb ðsÞVðsÞ ¼ Kb VðsÞ
ð3:13-33cÞ
y ðsÞ ¼ Gs ðsÞTðsÞ ¼
1 TðsÞ Js2
ð3:13-33dÞ
where Kt is the conversion constant of the position angle to an electrical signal, Gc ðsÞ is the transfer function of the controller, Kb is the constant of the thruster in which
134
Chapter 3
Figure 3.56
Satellite orientation control system. (a) Overall picture of satellite orientation control system; (b) schematic diagram of the closed-loop system; (c) block diagram of the closed-loop system.
the voltage VðsÞ is converted to the torque TðsÞ, and Gs ðsÞ ¼ 1=Js2 is the satellite’s transfer function, where J is the moment of inertia of the satellite about the X-axis. It is noted that relation (3.13-33d) does not involve friction terms, since the satellite is assumed to be in space. The transfer function of the closed-loop system is HðsÞ ¼
y ðsÞ Kt Kb Gc ðsÞ ¼ r ðsÞ Js2 þ Kt Kb Gc ðsÞ
ð3:13-34Þ
From relation (3.13-34) it follows that if the controller is just an amplifier, in which case Gc ðsÞ ¼ Ka , where Ka is the amplifier’s gain, then HðsÞ would have the form
Mathematical Models of Systems
HðsÞ ¼
3.14
Kt Kb Ka !2n ¼ ; Js2 þ Kt Kb Ka s2 þ !2n
135
!2n ¼ Kt Kb ka J1
ð3:13-35Þ
PROBLEMS
1. Derive the integrodifferential equations for the two electrical networks shown in Figure 3.57. 2. Obtain the transfer functions and the impulse responses for the circuits shown in Figure 3.58. 3. Derive the transfer functions for the operational amplifier circuits shown in Figure 3.59. 4. Determine the state equations for the systems shown in Figure 3.60. The blocks shown to the right or below the systems define the inputs, the state variables, and the outputs of the system. 5. Find the transfer functions and impulse responses for the systems of Problem 4. 6. Obtain a state-space model for each of the systems having the following transfer functions:
Figure 3.57
136
Chapter 3
Figure 3.58
HðsÞ ¼
s2 þ 4 ; s3 þ 4s þ 1
HðsÞ ¼
1 2 ; sþ1 sþ2
2s þ 1 s þ 4s þ 2s2 þ s þ 5 3 2 sþ2 2s þ 4 6 sðs þ 1Þ sðs þ 1Þ 7 7 HðsÞ ¼ 6 4 1 2 5 sþ1 sþ1
HðsÞ ¼
4
3
7. A thin glass-walled mercury thermometer has initial temperature 1 . At time t ¼ 0, it is immersed in a bath of temperature 0 . The thermometer instant temperature is m . Derive the state equations and the transfer function of the thermometer from the two network representations shown in Figure 3.61. The simplified network representation of Figure 3.61a, involves a capacitance C that stores heat and a resistance R that limits the heat flow. The more accurate network representation of Figure 3.61b involves the resistance Rg and the capa-
Mathematical Models of Systems
137
Figure 3.59 citance Cg of the glass, as well as the resistance Rm and the capacitance Cm of the mercury. 8. Find the transfer function and the impulse response of the system shown in Figure 3.62. 9. For the system shown in Figure 3.63, determine (a) the differential equation, (b) the transfer function, and (c) the state equations, considering that the input is the displacement uðtÞ of the cart and the output is the displacement yðtÞ of the mass. 10. An inverted pendulum mounted on a motor-driven cart is shown in Figure 3.64. Assume that the input to the system is the control force u applied to the cart and
138
Chapter 3
Figure 3.60 the two outputs are the angle of the rod from the vertical axis and the position x of the cart. Obtain a state-space representation of the system. 11. A schematic diagram of a pneumatic actuating valve is shown in Figure 3.65. Assume that at t ¼ 0, there is a control pressure change pc from the steady-state value. As a result, there is a change pv of the valve pressure. The valve pressure variation pv results in a change x of the valve displacement. Considering that pc is the input and x is the output of the system, determine the transfer function of the valve. Here, A is the area of the diaphragm and K is the spring constant. 12. A two-tank liquid-level system is shown in Figure 3.66. At steady state, the flow rate is Q and the fluid heights in tank 1 and tank 2 are H1 and H2 , respectively. Small flow rate changes at the input of tank 1, at the output of tank 1, and at the output of tank 2 and are defined as q, q1 , and q2 , respectively. Small variations of the liquid level of tank 1 and tank 2 from the steady-state values are defined as h1 and h2 , respectively. Assume that C1 and C2 are the capacitances of tanks 1 and 2, respectively, while R1 is the flow resistance between the two tanks and R2 is the flow resistance at the output of tank 2. The quantities C and R are defined as follows: Change in liquid stored (m3 Þ Change in liquid level Change in level difference (m) R¼ Change in flow rate (m3 =secÞ
C¼
and
Derive a state-space representation of the system when (a) q2 is considered to be the output and, (b) h2 is considered to be the output. 13. Find the transfer functions of the block diagrams shown in Figure 3.67. 14. Find the transfer functions of the signal-flow graphs shown in Figure 3.68. 15. For every system described in Section 3.13, find a state-space description.
Mathematical Models of Systems
Figure 3.60
(contd.)
139
140
Figure 3.61
Figure 3.62
Figure 3.63
Chapter 3
Figure 3.64
Figure 3.65
Figure 3.66
[16].
142
Figure 3.67
Chapter 3
Mathematical Models of Systems
Figure 3.67
(contd.)
143
144
Chapter 3
Figure 3.68
BIBLIOGRAPHY Books 1. 2. 3.
PJ Antsaklis, AN Michel. Linear Systems. New York: McGraw-Hill, 1997. RH Cannon Jr. Dynamics of Physical Systems. New York: McGraw-Hill, 1967. JJ D’Azzo, CH Houpis. Linear Control System Analysis and Design, Conventional and Modern. New York: McGraw-Hill, 1975. 4. PM DeRusso, RJ Roy, CM Close. State Variables for Engineers. New York: John Wiley, 1965. 5. JJ DiStefano III, AR Stubberud, IJ Williams. Feedback and Control Systems. Schaum’s outline series. New York: McGraw-Hill, 1967. 6. RC Dorf, RE Bishop. Modern Control Analysis. London: Addison-Wesley, 1995. 7. VW Eveleigh. Introduction to Control Systems Design. New York: McGraw-Hill, 1972. 8. TE Fortman, KL Hitz. An Introduction to Linear Control Systems. New York: Marcel Dekker, 1977. 9. GF Franklin, JD Powell, A Emami-Naeini. Feedback Control of Dynamic Systems. Reading MA: Addison-Wesley, 1986. 10. B Friedland. Control System Design. An Introduction to State-Space Methods. New York: McGraw-Hill, 1987. 11. R Johansson. System Modeling and Identification. Englewood Cliffs, New Jersey: Prentice Hall, 1993.
Mathematical Models of Systems 12. 13. 14. 15. 16. 17.
145
T Kailath. Linear Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1980. BC Kuo. Automatic Control Systems. London: Prentice Hall, 1995. AGC MacFarlane. Dynamic System Models. London: George G. Harrap and Co., 1970. NS Nise. Control Systems Engineering. New York: Benjamin and Cummings, 1995. K Ogata. Modern Control Systems. London: Prentice Hall, 1997. FH Raven. Automatic Control Engineering. 4th ed. New York: McGraw-Hill, 1987.
Articles 18.
LM Silverman. Realization of linear dynamical systems. IEEE Trans Automatic Control AC-16:554–567, 1971.
4 Classical Time-Domain Analysis of Control Systems
4.1
INTRODUCTION
Chapters 4 and 5 refer to the time-domain analysis of linear time-invariant control systems. In particular, Chap. 4 refers to single-input–single-output (SISO) systems and uses the classical system description techniques: namely, the differential equations and the transfer function. On the other hand, Chap. 5 refers to the general case of systems with many inputs and many outputs (MIMO), and uses the modern system description technique: namely, the state equations. The reason for studying both the classical as well as the modern analysis methods is that both methods have great theoretical and practical importance for control engineers. The problem of time-domain analysis may be briefly stated as follows: given the system (i.e., given a specific description of the system) and its input, determine the time-domain behavior of the output of the system. The basic motivation for system analysis is that one can predict (theoretically) the system’s behavior. 4.2
SYSTEM TIME RESPONSE
4.2.1
Analytical Expression of Time Response
Consider the SISO linear time-invariant system described by the differential equation yðnÞ þ an1 yðn1Þ þ þ a1 yð1Þ þ a0 y ¼ bm uðmÞ þ þ b1 uð1Þ þ b0 u
ð4:2-1Þ
with initial conditions yðkÞ ð0Þ ¼ ck , k ¼ 0; 1; 2; . . . ; n 1 and uðkÞ ð0Þ ¼ dk , k ¼ 0; 1; 2 ; . . . ; m 1 and m n. To find the general solution of Eq. (4.2-1) we proceed as follows: Apply the Laplace transform to Eq. (4.2-1); after some algebraic simplifications, we arrive at the following: aðsÞYðsÞ ðsÞ ¼ bðsÞUðsÞ ðsÞ
ð4:2-2Þ
where 147
148
Chapter 4
aðsÞ ¼ sn þ an1 þ þ a1 s þ a0 ; ðsÞ ¼
n X i1 X
bðsÞ ¼ bm sm þ bm1 sm1 þ þ b1 s þ b0
ai ck si1k ¼ n1 sn1 þ n2 sn2 þ þ 1 s þ 0
i¼0 k¼0
ðsÞ ¼
m X i1 X
bi dk si1k ¼ m1 sm1 þ m2 sm2 þ þ 1 s þ 0
i¼0 k¼0
It is pointed out that the polynomials aðsÞ and bðsÞ are functions of the coefficients ai and bi , respectively, while the polynomial ðsÞ is a function of both the coefficients ai and the initial conditions ck and the polynomial ðsÞ is a function of both the coefficients bi and the initial conditions dk . The solution of Eq. (4.2-2) is given by bðsÞ ðsÞ ðsÞ ðsÞ bðsÞ ðsÞ ¼ YðsÞ ¼ UðsÞ þ þ UðsÞ aðsÞ aðsÞ aðsÞ aðsÞ aðsÞ aðsÞ ¼ Yh ðsÞ þ Yf ðsÞ
ð4:2-3Þ
where Yh ðsÞ ¼
ðsÞ aðsÞ
and
Yf ðsÞ ¼
bðsÞ ðsÞ UðsÞ aðsÞ aðsÞ
Here, yh ðtÞ ¼ L1 fYh ðsÞg is called the natural or free response of the system and it is due solely to the initial conditions c0 ; c1 ck1 . In other words yh ðtÞ is the system’s response when there is no external excitation, i.e., when uðtÞ ¼ 0. It is for this reason that yh ðtÞ is also known as the homogeneous solution of the differential equation (4.21). On the other hand, yf ðtÞ ¼ L1 fYf ðsÞg is called the forced response and it is due solely to the input uðtÞ and its initial conditions d0 ; d1 ; . . . ; dm1 . The forced response yf ðtÞ is also known as the particular solution of the differential equation (4.2-1). Clearly, when the initial conditions c1 ; c2 ; . . . ; ck are all zero, then ðsÞ ¼ 0, and when the initial conditions d1 ; d2 ; . . . ; dm are all zero, then ðsÞ ¼ 0. When both ðsÞ and ðsÞ are zero or ðsÞ ðsÞ ¼ 0, then YðsÞ reduces to bðsÞ aðsÞ ð4:2-4Þ YðsÞ ¼ UðsÞ ¼ GðsÞUðsÞ; where GðsÞ ¼ aðsÞ bðsÞ where GðsÞ is the system’s transfer function. Let the input UðsÞ have the general form UðsÞ ¼
q q1 þ þ f1 s þ f0 f ðsÞ fq s þ fq1 s ¼ p p1 vðsÞ s þ vp1 s þ þ v1 s þ v0
In this case, Eq. (4.2-3) becomes YðsÞ ¼
bðsÞ f ðsÞ ðsÞ ðsÞ þ aðsÞvðsÞ aðsÞ aðsÞ
Also, let bðsÞ f ðsÞ ðsÞ ðsÞ ¼ þ aðsÞvðsÞ aðsÞ vðsÞ Then, YðsÞ becomes
ð4:2-5Þ
Time-Domain Analysis
149
ðsÞ ðsÞ ðsÞ ðsÞ ðsÞ ðsÞ þ þ ¼ þ ¼ Y1 ðsÞ þ Y2 ðsÞ ð4:2-6Þ aðsÞ vðsÞ aðsÞ aðsÞ aðsÞ vðsÞ
ðsÞ ðsÞ , Y ðsÞ ¼ , and ðsÞ ¼ ðsÞ þ ðsÞ ðsÞ. Further, assume that where Y1 ðsÞ ¼ aðsÞ 2 vðsÞ p n Y Y aðsÞ ¼ ðs i Þ and vðsÞ ¼ ðs i Þ YðsÞ ¼
i¼1
i¼1
To determine yðtÞ, we expand YðsÞ in partial fractions. Let Y1 ðsÞ ¼
n
ðsÞ X Ki ¼ aðsÞ s i i¼1
ðsÞ X Ri ¼ vðsÞ i¼0 s i p
and
Ys ðsÞ ¼
Then, yðtÞ ¼ L1 fYðsÞg is given by ( ) ( p ) p n n X X Ri X X Ki 1 1 yðtÞ ¼ L K i e i t þ Ri e i t þL ¼ s s
i i i¼0 i¼0 i¼0 i¼0
ð4:2-7Þ
¼ y1 ðtÞ þ y2 ðtÞ It is clear from Eq. (4.2-7) that the behavior of yðtÞ is crucially affected by the poles i of the system transfer function GðsÞ ¼ bðsÞ=aðsÞ and by the poles i of the input UðsÞ ¼ f ðsÞ=vðsÞ. The following definitions are introduced. Definition 4.2.1 The transient response ytr ðtÞ of a system is that particular part of the response of the system which tends to zero as time increases. This means tht ytr ðtÞ has the property lim ytr ðtÞ ¼ 0
t!1
Definition 4.2.2 The steady-state response yss ðtÞ of a system is that particular part of the response of the system which remains after the transient part has reached zero. The steady-state response may be a constant (e.g., yss ðtÞ ¼ c), a sinusoidal function with constant amplitude (e.g., yss ðtÞ ¼ A sin !t), a function increasing with time (e.g., yss ðtÞ ¼ ct or yss ðtÞ ¼ ct2 or yss ðtÞ ¼ At sin !t), etc. From the above two definitions it is clear that the total solution yðtÞ is the sum of the transient response and the steady-state response, i.e., yðtÞ ¼ ytr ðtÞ þ yss ðtÞ Now, let all poles i of GðsÞ lie on the left-hand complex plane, or equivalently, let the real part of all poles i be negative (Re i < 0). In this case (and this is usually the case) we say that the system is asumptotically stable (see Sec. 6.2). It is obvious that if Re i < 0, then lim ei t ¼ 0
t!1
Therefore, for asymptotically stable systems, the part y1 ðtÞ of the response yðtÞ of relation (4.2-7), where
150
Chapter 4
y1 ðtÞ ¼
n X
Ki ei t
i¼0
belongs to the transient response ytr ðtÞ of yðtÞ. This means that the contribution of ðsÞ, which is due to the initial conditions of the input uðtÞ, the contribution of ðsÞ, which is due to the initial conditions of the output yðtÞ, and the part of the response ðsÞ=aðsÞ, all appear only in the system’s transient response. Hence, in the steadystate response, only the part y2 ðtÞ of the response yðtÞ of relation (4.2-7) will appear, where y2 ðtÞ ¼
p X
Ri e i t
i¼0
for which Re i 0. The rest of y2 ðtÞ, for which Re i < 0, belongs to the transient response of yðtÞ. Remark 4.2.1 From the aforementioned material, we arrive at the conclusion that the waveform of yss ðtÞ of asymptotically stable systems solely depends upon the poles i of the input, for which Re i 0. In control systems, the input is usually the unit step input uðtÞ ¼ 1. In this case, we have Y2 ðsÞ ¼
ðsÞ 0 Gð0Þ b ¼ ¼ ¼ 0 ; vðsÞ s s a0 s
where
GðsÞ ¼
bðsÞ aðsÞ
For simplicity, assume that Gð0Þ ¼ b0 =a0 ¼ 1. Then, Y2 ðsÞ ¼ 1=s, and hence yss ðtÞ ¼ 1, i.e., the output follows exactly the input in the steady state. To illustrate the material of the present subsection, we present two simple examples. In the first example, in order to simplify the analysis, no use is made of the general procedure presented in this subsection. On the contrary, in the second example, the general procedure is illustrated by applying all steps to yield yðtÞ. Example 4.2.1 Consider the RL circuit shown in Figure 4.1 with initial conditions ið0Þ ¼ I0 . For simplicity, let the input voltage vðtÞ ¼ uðtÞ ¼ the unit step function, R ¼ 1 and L ¼ 1 H. Determine the total response of the circuit. Solution Kirchhoff’s voltage law is di di þ Ri ¼ vðtÞ or þ i ¼ uðtÞ; dt dt since R ¼ 1 ; L ¼ 1 H; and vðtÞ ¼ uðtÞ:
L
Here, the output yðtÞ ¼ vR ðtÞ ¼ Ri ¼ i. Substituting this relation in the differential equation yields dy þ y ¼ uðtÞ dt The above differential equation relates directly the input uðtÞ with the output yðtÞ. Take the Laplace transform of both sides of the differential equation to yield
Time-Domain Analysis
Figure 4.1
151
RL circuit.
1 sYðsÞ yð0Þ þ YðsÞ ¼ ; s
where yð0Þ ¼ vR ð0Þ ¼ Rið0Þ ¼ I0
Solving for YðsÞ gives YðsÞ ¼
1 yð0Þ 1 1 yð0Þ þ ¼ þ sðs þ 1Þ s þ 1 s s þ 1 s þ 1
Taking the inverse Laplace transform, yields yðtÞ ¼ 1 et þ yð0Þet The above expression for yðtÞ is the total response of the circuit. The right-hand side of the equation may be grouped as follows: yðtÞ ¼ yð0Þet þ ½1 et ¼ yh ðtÞ þ yf ðtÞ |fflfflfflfflffl{zfflfflfflfflffl} |fflfflffl{zfflfflffl} free response
forced response
or may be grouped as follows yðtÞ ¼ ½ yð0Þ 1et þ |fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl} transient response
1 |{z}
¼ ytr ðtÞ þ yss ðtÞ
steadystate response
These two ways of writing yðtÞ clearly demonstrate the four characteristics of the time response of a system: 1. 2. 3.
Free response yh ðtÞ ¼ the part of yðtÞ which is due to the initial condition yð0Þ. Forced response yf ðtÞ ¼ the part of yðtÞ which is due to the external excitation uðtÞ. Transient response ytr ðtÞ ¼ the part of yðtÞ which goes to zero fast.
152
Chapter 4
4.
Steady state yss ðtÞ ¼ the part of yðtÞ which remains after the transient response has died out.
Example 4.2.2 Consider a system described by the following differential equation: yð2Þ þ 3yð1Þ þ 2y ¼ 6uð1Þ þ 2u with initial conditions yð0Þ ¼ 1 and yð1Þ ð0Þ ¼ 2. Let uðtÞ ¼ e5t , in which case uð0Þ ¼ 1. To illustrate the general procedure presented in this subsection for determining the total time response of a system, apply this procedure, step by step, to solve the given differential equation. Solution Take the Laplace transform of both sides of the differential equation to yield ½s2 YðsÞ syð0Þ yð1Þ ð0Þ þ 3½sYðsÞ yð0Þ þ 2YðsÞ ¼ 6½sUðsÞ uð0Þ þ 2UðsÞ or ðs2 þ 3s þ 2ÞYðsÞ ½yð0Þs þ yð1Þ ð0Þ þ 3yð0Þ ¼ ð6s þ 2ÞUðsÞ 6uð0Þ Comparing the equation above with Eq. (4.2-2), we have aðsÞ ¼ s2 þ a1 s þ a0 ¼ s2 þ 3s þ 2 ¼ ðs þ 1Þðs þ 2Þ bðsÞ ¼ b1 s þ b0 ¼ 6s þ 2 ¼ 2ð3s þ 1Þ ðsÞ ¼ 1 s þ 0 ¼ yð0Þs þ yð1Þ ð0Þ þ 3yð0Þ ¼ s þ 5 ðsÞ ¼ 0 ¼ 6uð0Þ ¼ 6 The free response of the system is Yh ðsÞ ¼
ðsÞ sþ5 sþ5 4 3 ¼ ¼ ¼ aðsÞ s2 þ 3s þ 2 ðs þ 1Þðs þ 2Þ s þ 1 s þ 2
and hence yh ðtÞ ¼ L1 fYh ðsÞg ¼ 4et 3e2t The force response of the system is bðsÞ ðsÞ 2ð3s þ 1Þ 1 6 ¼ 2 UðsÞ 2 Yf ðsÞ ¼ aðsÞ aðsÞ s 5 s þ 3s þ 2 s þ 3s þ 2 32 16=3 32=7 16=21 ¼ þ þ ¼ ðs þ 1Þðs þ 2Þðs 5Þ sþ1 sþ2 s5 and hence yf ðtÞ ¼
16 t 32 2t 16 5t e þ e þ e 3 7 21
Therefore, the total time response of the system is
Time-Domain Analysis
153
yðtÞ ¼ yh ðtÞ þ yf ðtÞ
16 32 16 ¼ ½4et 3e2t þ et þ e2t þ e5t 3 7 21 4 t 11 2t 16 5t ¼ e þ e þ e 3 7 21
The transient response ytr ðtÞ involves all terms of yðtÞ which tend to zero as time increases. Therefore ytr ðtÞ ¼ 4et 3e2t
16 t 32 2t 4 11 e þ e ¼ et þ e2t 3 7 3 7
The steady-state response will then be yss ðtÞ ¼
16 5t e 21
Clearly yðtÞ ¼ yh ðtÞ þ yf ðtÞ ¼ ytr ðtÞ þ yss ðtÞ 4.2.2
Characteristics of the Graphical Representation of Time Response
The total system time response (i.e., both transient and steady state) of an asumptotically stable system with uðtÞ ¼ 1, has the general form shown in Figure 4.2. This waveform gives an overall picture of the system’s behavior in the time domain. The basic characteristics of this unit step response waveform are the following.
Figure 4.2
Typical unit step response of an asymptotically stable system.
154
Chapter 4
1 Maximum Overshoot This is the difference between the maximum value ym and the final value (i.e., the steady state yss ) of yðtÞ. The percentage v of the maximum overshoot is defined as y yss ð4:2-7Þ v% ¼ 100 m yss 2 Delay Time Td This is the time required for yðtÞ to reach half of its final value. 3 Rise Time Tr This is the time required for yðtÞ to rise from 10% to 90% of its final value. 4 Settling Time Ts This is the time required for yðtÞ to reach and remain within a certain range of its final value. This range is usually from 2–5% of the amplitude of the final value. 4.3
TIME RESPONSE OF FIRST- AND SECOND-ORDER SYSTEMS
In Sec. 4.2 we presented a rather general method of analyzing linear systems. In this section we present a detailed analysis of first- and second-order systems with zero initial conditions, since these type of systems are often met in practice. In particular, we derive the analytical expressions of the responses for first- and second-order systems. We also study the effects of the transfer function parameters on the system’s output waveform, aiming in waveform standardization. It is shown that the standardization is easy for first- and second-order systems, but becomes more and more difficult as the order of the system increases. 4.3.1
First-Order Systems
The transfer function of a first-order system has the general form GðsÞ ¼
b0 K ¼ s þ a0 Ts þ 1
ð4:3-1Þ
where K is the amplification constant and T is the time constant. The system response YðsÞ, when UðsÞ ¼ 1=s and K ¼ 1, will be YðsÞ ¼ 1=s ðTs þ 1Þ: If we expand YðsÞ in partial fractions, we obtain YðsÞ ¼
1 1 ; s s þ 1=T
and hence yðtÞ ¼ 1 eðt=TÞ
ð4:3-2Þ
It is clear that first-order systems involve no oscillations and the waveform yðtÞ is affected only by the time constant T. The waveforms of y1 ðtÞ, y2 ðtÞ, and y3 ðtÞ, which correspond to the time constants T1 < T2 < T3 , are given in Figure 4.3. The conclusion derived from Figure 4.3 is that the smaller the T, the higher the speed response of the system, where the term speed response is defined as the amount of
Time-Domain Analysis
155
time required for yðtÞ to reach 63.78% of its final value. For first-order systems, this amount of time coincides with the time constant T, since yðTÞ ¼ 1 e1 ¼ 0:6378: 4.3.2
Second-Order Systems
Consider a second-order system with transfer function !2n s2 þ 2!n s þ !2n
GðsÞ ¼
ð4:3-3Þ
where the constants !n and are called the natural undamped frequency and the damping ratio of the system, respectively. The poles of GðsÞ are pffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffi s1;2 ¼ !n 2 1 ¼ !n j!n 1 2 ¼ j!d pffiffiffiffiffiffiffiffiffiffiffiffiffi where ¼ !n and !d ¼ !n 1 2 are called the attenuation or damping constant and the damped natural frequency of the system, respectively. The system’s response YðsÞ, when UðsÞ ¼ 1=s, is as follows: YðsÞ ¼
!2n sðs2 þ 2! þ !2n Þ
ð4:3-4Þ
Depending on the value, or range of values, of the damping ratio , we distinguish the following four cases: Case 1 ð ¼ 0Þ In this case the poles of GðsÞ are imaginary since s1;2 ¼ j!n and relation (4.3-4) becomes
Figure 4.3
Unit step response of a first-order linear system.
156
Chapter 4
YðsÞ ¼
!2n sðs2 þ !2n Þ
If we expand YðsÞ in partial fractions, we have YðsÞ ¼
1 s ; s s2 þ !2n
and thus yðtÞ ¼ 1 cos !n t
ð4:3-5Þ
Thus, when ¼ 0 we observe that the response yðtÞ is a sustained oscillation with constant frequency to !n and constant amplitude equal to 1 (see Figure 4.4). In this case, we say that the system is undamped. Case 2 ð0 < < 1Þ In this case the poles of GðsÞ are a complex conjugate pair since s1;2 ¼ j!d and relation (4.3-4) becomes
Figure 4.4
Unit step response of a second-order linear system.
Time-Domain Analysis
YðsÞ ¼
157
!2n s½ðs þ Þ2 þ !2d
If we expand YðsÞ in partial fractions, we have
# " 1 s þ 2 1 sþ !d YðsÞ ¼ ¼ s ðs þ Þ2 þ !2d s ðs þ Þ2 þ !2d !d ðs þ Þ2 þ !2d
Taking the inverse Laplace transform, we have yðtÞ ¼ 1 et cos !d t et sin !d t !d The above expression for yðtÞ may be written as et yðtÞ ¼ 1 pffiffiffiffiffiffiffiffiffiffiffiffiffi sinð!d t þ ’Þ; 1 2
where
1
’ ¼ tan
pffiffiffiffiffiffiffiffiffiffiffiffiffi 1 2
ð4:3-6Þ
Thus, when 0 < < 1 we observe that the response yðtÞ is a damped oscillation which tends to 1 as t ! 1 (see Figure 4.4). In this case, we say that the system is underdamped. Case 3 ð ¼ 1Þ In this case the poles of G(s) are the real double pole !n , and relation (4.3-4) becomes YðsÞ ¼
!2n sðs þ !n Þ2
If we expand YðsÞ in partial fractions, we have YðsÞ ¼
1 1 !2n ; s s þ !n ðs þ !n Þ2
and thus yðtÞ ¼ 1 e!n t !n te!n t ð4:3-7Þ
Thus, when ¼ 1 we observe that the waveform of the response yðtÞ involves no oscillations, and asymptotically tends to 1 as t ! 1 (see Figure 4.4). In this case we say that the system is critically damped. Case 4 ð > 1Þ
pffiffiffiffiffiffiffiffiffiffiffiffiffi In this case the poles of GðsÞ are both real and negative since s1:2 ¼ !n 2 1 and relation (4.3-4) becomes !2n s½ðs þ Þ2 a2 pffiffiffiffiffiffiffiffiffiffiffiffiffi where a ¼ !n 2 1. If we expand YðsÞ in partial fractions, we have h i 1 sþ a YðsÞ ¼ s ðs þ Þ2 a2 a ðs þ Þ2 a2 YðsÞ ¼
Taking the inverse Laplace transform, we have
158
Chapter 4
yðtÞ ¼ 1 et cosh at et sinh at a " # pffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffi t ¼1e cosh !n 2 1 t þ pffiffiffiffiffiffiffiffiffiffiffiffiffi sinh !n 2 1 t 2 1
ð4:3-8Þ
Thus, when > 1 we observe that the response yðtÞ involves no oscillations and tends asymptotically to 1 as t ! 1, while the speed response decreases as becomes larger (see Figure 4.4). In this case, we say that the system is overdamped. Summarizing the above results we conclude that when ¼ 0, we have sustained (undamped) oscillations. As increases towards 1, these oscillations are damped more and more. When reaches 1, the oscillations stop. Finally, as further increases becoming greater than 1, we have no oscillations and the output requires more and more time to reach asymptotically the value 1. 4.3.3
Special Issues for Second-Order Systems
Here we will study certain issues regarding second-order systems which are of particular interest. 1 The Root Locus The term root locus (see Chap. 7) is defined as the locus of all roots of the characteristic polynomial pðsÞ of a system in the complex plane. This locus is formed when varying one or more system parameters. Consider a system described by its transfer function GðsÞ, where pðsÞ is the denominator of GðsÞ. For second-order systems, pðsÞ has the form pðsÞ ¼ s2 þ 2!n s þ !2n
ð4:3-9Þ pffiffiffiffiffiffiffiffiffiffiffiffiffi 2 The roots of pðsÞ are s1;2 ¼ !n !n 1. In Figure 4.5 the root locus of these two roots is shown, as varies from 1 to þ1. 2 The Time Response as a Function of the Positions of the Two Poles In Figure 4.6 several typical positions of the poles of relation (4.3-9) and the corresponding unit-step responses are given. 3 Relation Between the Damping Ratio and the Overshoot We have found that when 0 < < 1 the response yðtÞ has the analytical form (4.3-6), while its waveform presents an overshoot. To determine this overshoot it suffices to determine the maximum value ym of yðtÞ. To this end, we take the derivative of relation (4.3-6) to yield et !n et ffi sin !d t yð1Þ ðtÞ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi ½ sinð!d t þ ’Þ !d cosð!d t þ ’Þ ¼ pffiffiffiffiffiffiffiffiffiffiffiffi 1 2 1 2 Here yð1Þ ðtÞ is zero when sin !d t ¼ 0. Furthermore, sin !d t is zero when !d tk ¼ k, where k ¼ 1; 2; . . . ; i.e., when tk ¼
k k ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi ; !d !n 1 2
k ¼ 1; 2; . . .
Hence, the expression for yðtk Þ is
Time-Domain Analysis
Figure 4.5
159
The root locus of a second-order system.
pffiffiffiffiffiffiffiffiffiffiffiffiffi exp k= 1 2 pffiffiffiffiffiffiffiffiffiffiffiffiffi sinðk þ ’Þ yðtk Þ ¼1 1 2 pffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ 1 þ ð1Þk1 exp k= 1 2 ;
k ¼ 1; 2; . . .
Clearly, yðtk Þ becomes maximum for k ¼ 1; 3; 5 . . . and minimum for k ¼ 2; 4; 6; . . . (see Figure 4.7a). Therefore, the maximum value ym is given by pffiffiffiffiffiffiffiffiffiffiffiffiffi ym ¼ 1 þ exp = 1 2 ð4:3-10Þ which occurs when k ¼ 1, i.e., at the time instant t1 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi !n 1 2 The overshoot percentage defined in Eq. (4.2-7) will be pffiffiffiffiffiffiffiffiffiffiffiffiffi v% ¼ 100 exp = 1 2
ð4:3-11Þ
ð4:3-12Þ
The overshoot v as a function of the damping ratio is given in Figure 4.7b. 4.4
MODEL SIMPLICATION
The model simplification or model reduction problem may be stated as follows. We are given a detailed mathematical model of a system, which is usually of very high order and hence very difficult to work with. We wish to find a simpler model, which approximates the original model satisfactorily. Clearly, the simpler model has the advantage in that it simplifies the system description, but it has the disadvantage in that it is less accurate than the original detailed model. The motivation for deriving simplified models may be summarized as follows: 1.
It simplifies the description and the analysis of the system
160
Figure 4.6
Chapter 4
Unit step response comparison for various pole locations in the s-plane of a second-order system.
Time-Domain Analysis
Figure 4.7
2. 3. 4.
161
(a) The maximum overshoot and (b) its percentage as a function of .
It simplifies the computer simulation of the system It facilitates the controller design problem and yields controllers with simpler structures It reduces the computational effort in the analysis and design of control systems.
Obviously, the order of the simplified model is much less than that of the original model. For this reason the model simplification problem is also known as the order reduction problem. Many techniques have been proposed to simplify a model [36–39]. This section aims to introduce the reader to this interesting problem. To this end, we will present here one of the simplest techniques known as the dominant pole method. The dominant pole method is as follows. Let GðsÞ be the transfer function of the original system. Expand GðsÞ in partial fractions to yield GðsÞ ¼
c1 c2 cn þ þ þ s 1 s 2 s n
where 1 ; 2 ; . . . ; n are the poles of GðsÞ. Let GðsÞ be asymptotically stable, i.e., let Re i < 0, for i ¼ 1; 2; . . . ; n. Then, the closer any pole i is to the imaginary axis, the greater the effect on the system response and vice versa; i.e., the farther away i is from the imaginary axis, the less the effect on the system response. For this reason, the poles that are located close to the imaginary axis are called dominant poles. The dominant pole simplification method yields a simplified model involving only the dominant poles. To illustrate the dominant pole approach, consider a system with input signal uðtÞ ¼ 1 and transfer function GðsÞ ¼ Then
1 1 þ ; s þ 1 s þ 2
where 1 and 2 positive and 1 2 :
162
Chapter 4
1 1 þ YðsÞ ¼ GðsÞUðsÞ ¼ sðs þ 1 Þ sðs þ 1 Þ 1 1 1 1 1 1 þ ¼ 1 s s þ 1 2 s s þ 2 Hence yðtÞ ¼
1 1 ð1 e1 t Þ þ ð1 e2 t Þ 1 2
Here, 1 is closer to the imaginary axis than 2 . Hence, the term e2 t goes to zero much faster than e1 t . For this reason the dominant pole 1 is often called slow mode and 2 is called fast mode. Let G1 ðsÞ be the satisfactory approximant of GðsÞ sought. Then, since e2 t goes to zero much faster than e1 t , it is clear that G1 ðsÞ should ‘‘keep’’ the slow mode 1 and ‘‘drop’’ the fast mode 2 . For this reason, we choose as simplified model the transfer function G1 ðsÞ, where G1 ðsÞ ¼
1 s þ 1
Clearly, G1 ðsÞ is simpler than the original GðsÞ and involves the most dominant pole 1 . The time response y1 ðtÞ of the simplified model is given by y1 ðtÞ ¼ L1 fG1 ðsÞUðsÞg ¼
1 ð1 e1 t Þ 1
Example 4.4.1 Consider the transfer function GðsÞ ¼
A1 A A3 A4 A5 þ 2 þ þ þ s þ 1 s þ 2 s þ 10 s þ 100 s þ 1000
Find a simplified model of third and second order using the dominant pole method. Solution The dominant poles of GðsÞ, in decreasing order, are 1, 2, and 10. Let G3 ðsÞ and G2 ðsÞ be the third- and the second-order approximants of GðsÞ. Then, the simplified models sought are the following: G3 ðsÞ ¼
A1 A A3 þ 2 þ s þ 1 s þ 2 s þ 10
and
G2 ðsÞ ¼
A1 A þ 2 sþ1 sþ2
For simplicity, let A1 ¼ A2 ¼ A3 ¼ A4 ¼ A5 ¼ 1. Also, let the input signal uðtÞ ¼ 1. Then, the output yðtÞ of the original system GðsÞ and the output y3 ðtÞ and y2 ðtÞ of the reduced-order systems G3 ðsÞ and G2 ðsÞ, respectively, may be determined as follows: For yðtÞ, we have 1 1 1 1 1 1 þ þ þ þ YðsÞ ¼ s s þ 1 s þ 2 s þ 10 s þ 100 s þ 1000 1 1 1 1 1 1 1 1 ¼ þ þ s sþ1 2 s sþ2 10 s s þ 10 1 1 1 1 1 1 þ þ 100 s 100 1000 s 1000
Time-Domain Analysis
163
and hence 1 1 1 ½1 e10t þ 100 ½1 e100t þ 1000 ½1 e1000t yðtÞ ¼ ½1 et þ 12 ½1 e2t þ 10
For y3 ðtÞ and y2 ðtÞ we keep only the first three and two brackets in the above expression, respectively, i.e., 1 ½1 e10t y3 ðtÞ ¼ ½1 et þ 12 ½1 e2t þ 10
y2 ðtÞ ¼ ½1 et þ 12 ½1 e2t In Figure 4.8 the plots of yðtÞ, y3 ðtÞ, and y2 ðtÞ are given. Comparison of these three plots shows that both y3 ðtÞ and y2 ðtÞ are close to yðtÞ. For better accuracy, one may choose G3 ðsÞ as an approximant of GðsÞ. For greater simplicity one may choose G2 ðsÞ. Clearly, in choosing either G3 ðsÞ or G2 ðsÞ, we reduce the order of the original system from five to three ðG3 ðsÞÞ or to two ðG2 ðsÞÞ.
4.5
COMPARISON BETWEEN OPEN- AND CLOSED-LOOP SYSTEMS
There are important differences between open- and closed-loop systems. Three of these differences are of paramount importance and they are described below. From these differences we conclude that closed-loop systems are superior over open-loop systems and, for this reason, they are more often used in practice.
Figure 4.8
Plots of yðtÞ; y3 ðtÞ, and y2 ðtÞ.
164
4.5.1
Chapter 4
Effect on the Output Due to Parameter Variations in the OpenLoop System
Consider the closed-loop system shown in Figure 4.9, where GðsÞ is the transfer function of the system under control and FðsÞ is the feedback transfer function. The open-loop system’s output YðsÞ (i.e., when FðsÞ ¼ 0) is YðsÞ ¼ GðsÞRðsÞ The closed-loop system’s output Yc ðsÞ (i.e., when FðsÞ 6¼ 0Þ is GðsÞ Yc ðsÞ ¼ RðsÞ 1 þ GðsÞFðsÞ
ð4:5-1Þ
ð4:5-2Þ
Now, assume that certain parameters of the transfer function GðsÞ undergo variations. Let dGðsÞ be the change in GðsÞ due to these parameter variations. As a result, the output YðsÞ will also vary. In particular, for the open-loop system (4.5-1) the change dYðsÞ of the output YðsÞ will be dYðsÞ ¼ RðsÞdGðsÞ
ð4:5-3Þ
while for the closed-loop system (4.5-2) the change dYc ðsÞ of the output Yc ðsÞ will be RðsÞ dGðsÞ ð4:5-4Þ dYc ðsÞ ¼ ½1 þ GðsÞFðsÞ2 If we divide relations (4.5-3) and (4.5-1), we have dYðsÞ dGðsÞ ¼ YðsÞ GðsÞ Similarly, if we divide relations (4.5-4) and (4.5-2), we have dYc ðsÞ 1 dGðsÞ ¼ Yc ðsÞ 1 þ GðsÞFðsÞ GðsÞ Next, consider the magnitudes in Eqs (4.5-5) and (4.5-6). We have dYðsÞ dGðsÞ ¼ YðsÞ GðsÞ dGðsÞ dYc ðsÞ 1 ¼ Y ðsÞ j1 þ GðsÞFðsÞj GðsÞ c
Figure 4.9
Closed-loop system.
ð4:5-5Þ
ð4:5-6Þ
ð4:5-7Þ ð4:5-8Þ
Time-Domain Analysis
165
In control systems, we usually work in low frequencies, in which case we have that jGðsÞFðsÞj 1. This readily yields jdYc ðsÞj jdYðsÞj
ð4:5-9Þ
Releation (4.5-9) reveals that the effects of parameter changes of the transfer function GðsÞ on the closed-loop system’s output Yc ðsÞ is much smaller than that its effects on the open-loop system’s output YðsÞ. This property is one of the most celebrated basic advantages of closed-loop systems over open-loop systems. 4.5.2
Effect on the Output Due to Parameter Variations in the Feedback Transfer Function
Assume that the parameters of FðsÞ undergo certain variations. If we differentiate, relation (4.5-2) yields " # G2 ðsÞRðsÞ dYc ðsÞ ¼ dFðsÞ ð4:5-10Þ ½1 þ GðsÞFðsÞ2 If we divide relations (4.5-10) and (4.5-2), we have dYc ðsÞ dYc ðsÞ GðsÞ ¼ jGðsÞFðsÞj dFðsÞ ¼ dFðsÞ or Yc ðsÞ 1 þ GðsÞFðsÞ Yc ðsÞ j1 þ GðsÞFðsÞj FðsÞ ð4:5-11Þ Since jGðsÞFðsÞj 1, it follows that jGðsÞFðsÞj ’1 j1 þ GðsÞFðsÞj Hence, relation (4.5-11) becomes dYc ðsÞ dFðsÞ ’ Y ðsÞ FðsÞ c
ð4:5-12Þ
Relation (4.5-12) indicates that the variation dFðsÞ crucially affects the system’s output. For this reason the feedback transfer function FðsÞ must be made up of elements which should vary as little as possible. 4.5.3
Effect of Disturbances
Consider the two systems shown in Figure 4.10b, where we assume the presence of the disturbance (or noise) DðsÞ. For the open-loop system of Figure 4.10a, the portion Yd ðsÞ of the output which is due to the disturbance DðsÞ will be Yd ðsÞ ¼ G2 ðsÞDðsÞ Similarly for the closed-loop system of Figure 4.10, we have that G2 ðsÞ Ycd ðsÞ ¼ DðsÞ 1 þ G1 ðsÞG2 ðsÞ
ð4:5-13Þ
ð4:5-14Þ
where Ycd ðsÞ is the output Yc ðsÞ of the closed-loop system due to the disturbance DðsÞ. From relations (4.5-13) and 4.5-14) we have that
166
Chapter 4
Figure 4.10
(a) Open- and (b) closed-loop systems with disturbances.
1 Y ðsÞ Ycd ðsÞ ¼ 1 þ G1 ðsÞG2 ðsÞ d
ð4:5-15Þ
Taking under consideration that jG1 ðsÞG2 ðsÞj 1, yields jYcd ðsÞj jYd ðsÞj
ð4:5-16Þ
Relation (4.5-16) reveals another advantage of the feedback action, i.e., of the closed-loop system over the open-loop system. Specifically, it shows that the output of a closed-loop system is much less sensitive to disturbances than that of an openloop system. This advantage of the closed-loop systems is of paramount importance in practical control applications.
4.6
SENSITIVITY TO PARAMETER VARIATIONS
In the previous section, we dealt with systems or processes which are very often subject to parameter variations, which, in turn have undesirable effects upon the performance of the system. These variations in GðsÞ are usually due to component aging, changing in the environment, inevitable errors in the system or process model (e.g., in the parameters of its transfer function GðsÞ), and other factors. In Subsec. 4.5.1 it was assessed that in the open-loop case, changes in GðsÞ have a serious effect on the output YðsÞ. In the closed-loop case, this effect is considerably reduced, a fact which makes the closed-loop configuration much more attractive in practice than the open-loop configuration. In Subsec. 4.5.1, we studied the effect upon the output due to parameter variations. In this section we focus our attention on the determination of the sensitivity of the transfer function and of the poles of the closed-loop system due to parameter variations in GðsÞ. In this section we seek to establish techniques which yield the magnitude of the sensitivity of the transfer function and of the poles of the closed-loop system due to parameter variations.
Time-Domain Analysis
4.6.1
167
System Sensitivity to Parameter Variations
Definition 4.6.1 The closed-loop system sensitivity, designated by SGH is defined as the ratio of the percentage change in the closed-loop transfer function HðsÞ to the percentage change in the system transfer function GðsÞ, for a small incremental change. That is SGH ¼
HðsÞ=HðsÞ GðsÞ=GðsÞ
ð4:6-1Þ
In the limit, as the incremental changes go to zero, Eq. (4.6-1) becomes S¼
@H=H @ ln H ¼ @G=G @ ln G
ð4:6-2Þ
For the open-loop case, we have that HðsÞ ¼ GðsÞ and, hence, the sensitivity SGG of the open-loop system is SGG ¼
@HðsÞ=HðsÞ @GðsÞ=GðsÞ ¼ ¼1 @GsÞ=GðsÞ @GðsÞ=GðsÞ
ð4:6-3Þ
For the closed-loop case, we will consider the following two problems: the sensitivity of the closed system with respect to the changes in GðsÞ, denoted as SGH , and the sensitivity of the closed system with respect to the changes in FðsÞ, denoted as SFH . To this end, consider the closed-loop transfer function HðsÞ ¼ Hence, SGH
GðsÞ 1 þ GðsÞFðsÞ
ð4:6-4Þ
@HðsÞ=HðsÞ GðsÞ @HðsÞ 1 þ GðsÞFðsÞ @HðsÞ ¼ ¼ GðsÞ ¼ @GðsÞ=GðsÞ HðsÞ @GðsÞ GðsÞ @GðsÞ 1 1 ¼ ½1 þ GðsÞFðsÞ ¼ 2 1 þ GðsÞFðsÞ ½1 þ GðsÞFðsÞ
ð4:6-5Þ
Similarly, @HðsÞ=HðsÞ FðsÞ @HðsÞ 1 þ GðsÞFðsÞ @HðsÞ ¼ ¼ FðsÞ @FðsÞ=FðsÞ HðsÞ @FðsÞ GðsÞ @FðsÞ # " 2 FðsÞ½1 þ GðsÞFðsÞ G ðsÞ GðsÞFðsÞ ¼ ¼ 2 GðsÞ 1 þ GðsÞFðsÞ ½1 þ GðsÞFðsÞ
SFH ¼
ð4:6-6Þ
Clearly, when GðsÞFðsÞ 1, then SGH ! 0 SGG
and SFH
SFH ! 1
ð4:6-7Þ
Hence and indicate that the system is very sensitive to changes in GðsÞ in the open-loop system case and also very sensitive to changes in FðsÞ in the closed-loop system case, respectively. On the contrary, SGH indicates that the closed-loop system is very insensitive to changes in GðsÞ. These remarks are in complete agreement with the results of Sec. 4.5.
168
Chapter 4
4.6.2
Pole Sensitivity to Parameter Variations
Definition 4.6.2 The closed-loop pole sensitivity, denoted as S s , is the ratio of the change in the position s of the corresponding root of the characteristic equation of the closed-loop system to the change in the parameter , for a small increment change, i.e., S s ¼
ds d
ð4:6-8Þ
Definition 4.6.2 is very useful in determining the sensitivity of the roots of the characteristic equation (and hence the sensitviity of the poles) of the closed-loop system due to variations in a certain parameter. In computing Eq. (4.6-8), one may find the sensitivity to be, for example, very high, in which case one should take appropriate steps to reduce it. Example 4.6.1 Consider a typical second-order closed-loop system given in Figure 4.11. Find: a.
The sensitivity of the transfer function of the closed-loop system with respect to the gain K and to the parameter a (here, 1=a is the time constant of the open-loop system). b. The sensitivity of the roots r1 and r2 with respect to K and a. The nominal values of K and a are K ¼ 20 and a ¼ 4. c. Let K ¼ 4 and a ¼ 2 be the variations in K and a, respectively, in which case the new values of K and a are K ¼ 20 þ K ¼ 24 and a ¼ 4 þ a ¼ 6. Using the sensitivity approach, find the approximate values of r1 and r2 for each variation K and a, separately, and compare them with the exact values. Repeat the same step when both variations K and a take place simultaneously. Solution a. The transfer function HðsÞ of the closed-loop system is HðsÞ ¼
YðsÞ GðsÞ K ¼ ¼ RðsÞ 1 þ GðsÞFðsÞ sðs þ aÞ þ K
The sensitivity of HðsÞ with respect to K is given by
Figure 4.11
Typical second-order system with unity feedback.
Time-Domain Analysis
SKH ¼
169
K dH K½sðs þ aÞ þ K sðs þ aÞ þ K K sðs þ aÞ ¼ ¼ H dK K sðs þ aÞ þ K ½sðs þ aÞ þ K2
Similarly, the sensitivity of HðsÞ with respect to a is given by h a idH a½sðs þ aÞ þ K sK as SaH ¼ ¼ ¼ H da K sðs þ aÞ þ K ½sðs þ aÞ þ K2 Clearly, both SKH and SaH are functions of s. b. The characteristic equation of the closed-loop system is sðs þ aÞ þ K ¼ ðs r1 Þðs r2 Þ ¼ 0 Taking the derivative with respect to K yields 2s
ds ds þa þ1¼0 dK dK
and hence SKs ¼
ds 1 ¼ dK a þ 2s
For the nominal values K ¼ 20 and a ¼ 4, the roots of the characteristic equation are r1 ¼ 2 þ j4 and r2 ¼ 2 4j. Hence, the root sensitivity SKs with respect to K for the root r1 (i.e., for s ¼ r1 ) may be determined as follows: ds dr1 1 1 j r1 ¼ ¼ ¼ ¼ SK ¼ dK s¼r1 dK a þ 2s s¼r1 4 þ 2ð2 þ j4Þ 8 Similarly, for the sensitivity of r2 (i.e., for s ¼ r2 ): ds dr2 1 1 j ¼ ¼ ¼ ¼ SKr2 ¼ dK s¼r2 dK a þ 2ss¼r2 4 þ 2ð2 j4Þ 8 Next, the sensitivity Sas of the roots r1 and r2 of the characteristic polynomial with respect to a will be determined. To this end, take the derivative of the characteristic equation sðs þ aÞ þ K ¼ 0 with respect to a to yield ds d ds ds da ¼0 2s þ ½as ¼ 2s þ a þ s da da da da da and hence Sas ¼
ds s ¼ da a þ 2s
The sensitivity of the root r1 (i.e., for s ¼ r1 ) is Sar1 ¼
dr1 ð2 þ j4Þ 2þj ¼ ¼ 4 da 4 þ 2ð2 þ j4Þ
Similarly, for the root r2 (i.e., for s ¼ r2 ): Sar2 ¼
dr2 2j ¼ 4 da
c. We consider the following three cases: 1. K ¼ 4 and a ¼ 0. In this case, we make use of the relation
170
Chapter 4
dr1 ¼
dr1 dK ¼ SKr1 dK dK
Using this relation, one may approximately determine r1 , when K varies, as follows: j r1 ffi SKr1 K ¼ K 8 For K ¼ 4, the above expression gives r1 ffi 0:5j. Hence, the new value r~1 of r1 due to K ¼ 4 is given by r~1 ffi r1 þ r1 ¼ 2 þ j4:5. To find the exact value r1 for r1 , solve the pffiffiffiffifficharacteristic equation for K ¼ 20 þ 4 ¼ 24 and a ¼ 4 to yield r1 ¼ 2 þ j 20 ¼ 2 þ j4:47. Comparing the results, we observe that the approximate value r~1 is very close to the exact value r1 . 2. K ¼ 0 and a ¼ 2. As in case 1, we use the relation dr1 da ¼ Sar1 da dr1 ¼ da Using this relation, we determine r1 , when a varies, as follows: r1 ffi Sar1 a ¼
2þj a 8
For a ¼ 2, the new value r~1 of r1 due to a ¼ 2, is given by r~1 ffi r1 þ r1 ¼ ð2 þ j4Þ
ð2 þ jÞ 2 ¼ 3 þ j3:5 4
To find the exact value r1 of r1p , solve ffiffiffiffiffi the characteristic equation for K ¼ 20 and a ¼ 4 þ 2 ¼ 6 to yield r1 ¼ 3 þ j 11 ¼ 3 þ j3:32. Comparing the results, we observe that the approximate value r1 is very close to its exact value. 3. K ¼ 4 and a ¼ 2. In this case both K and a change simultaneously. For this case, we have @s @s ds ¼ dK þ da @K @a An approximate expression of the above equation is s ffi
@s @s K þ a ¼ SKs K þ Sas a @K @a
When K ¼ 4 and a ¼ 2, we have j 2þj 2 ¼ 1 r1 ffi 4 8 4 Hence, the new value r^1 of r1 due to K ¼ 4 and a ¼ 2 is given by r^1 ¼ ð2 þ j4Þ ¼ 3 þ j4. To find the exact value r1 of r1 , solve the characteristic pffiffiffiffiffi equation for K ¼ 20 þ 4 ¼ 24 and a ¼ 4 þ 2 ¼ 6 to yield r1 ¼ 3 þ j 15 ¼ 3 þ j3:87. Comparing the results, we observe that the approximate value r^1 is very close to the exact value r1 . Application of the above procedure yields analogous results for the root r2 .
Time-Domain Analysis
4.7
171
STEADY-STATE ERRORS
In this section, the behavior of the steady-state performance of closed-loop systems is studied. In the design of a control system the steady-state performance is of special significance, since we seek a system whose output yðtÞ, among other things, has a prespecified desired steady-state value yss ðtÞ. This desired yss ðtÞ is usually the steadystate value rss ðtÞ of the input (command) function rðtÞ. That is, control systems are designed in such a way that when they are excited by rðtÞ, they ‘‘follow’’ this input rðtÞ in the steady state as closedly as possible, which means that in the steady state it is desired to have yss ðtÞ ¼ rss ðtÞ. If yss ðtÞ is not exactly equal to rss ðtÞ, then an error appears, which is called the steady-state error. The determination of the steady-state error is the subject of this section. 4.7.1
Types of Systems and Error Constants
Consider the unity feedback closed-loop system of Figure 4.12. The general case of nonunity feedback systems is presented in Figure 4.13a, which may readily be reduced to unity feedback as shown in Figure 4.13b. The material of this section is based on the configuration of unity feedback of Figure 4.12. To facilitate the study of nonunity feedback systems, use can be made of its equivalent unity feedback system of Figure 4.13b. Consider the unity feedback system of Figure 4.12 and assume that GðsÞ has the form m Y ðTi0 s þ 1Þ
GðsÞ ¼ K
i¼1 q Y ðT sj
; is
where j þ q ¼ n m
ð4:7-1Þ
þ 1Þ
i¼1
The following definitions are useful. Definition 4.7.1 A system is called type j system when GðsÞ has j poles at the point s ¼ 0, in which case GðsÞ has the general form (4.7-1). Definition 4.7.2 The position (or step) error constant Kp of a system is defined as Kp ¼ lim GðsÞ. s!0 Hence, the cosntant Kp takes on the values
Figure 4.12
Unity feedback system.
172
Chapter 4
Figure 4.13
(a) Nonunity feedback system and (b) equivalent unity feedback system. m Y
Kp ¼ lim GðsÞ ¼ lim K s!0
s!0
ðTj0 s þ 1Þ
i¼1 q Y ðTi s sj i¼1
¼
þ 1Þ
K when j ¼ 0 1 when j > 0
ð4:7-2Þ
Definition 4.7.3 The speed (or velocity, or ramp) error constant Kv of a system is defined as Kv ¼ lim sGðsÞ. Hence, the constant Kv takes on the values s!0
m Y ðTi0 s þ 1Þ
Kv ¼ lim sGðsÞ ¼ lim K s!0
s!0
2
0 when j ¼ 0 4 K when j ¼ 1 ¼ q Y 1 when j > 1 s j1 ðT s þ 1Þ i¼1
ð4:7-3Þ
i
i¼1
Definition 4.7.4 The acceleration (or parabolic) error constant Ka of a system is defined as Ka ¼ lim s2 GðsÞ. Hence, the constant Ka takes on the values s!0
m Y
Ka ¼ lim s2 GðsÞ ¼ lim K s!0
s!0
ðTi0 s þ 1Þ
2
0 when j ¼ 0; 1 4 K when j ¼ 2 ¼ q Y 1 when j > 2 s j2 ðT s þ 1Þ i¼1
i
i¼1
ð4:7-4Þ
Time-Domain Analysis
4.7.2
173
Steady-State Errors with Inputs of Special Forms
Consider the closed-loop system of unity feedback of Figure 4.12. The system error eðtÞ studied in this section is defined as the difference between the command signal rðtÞ and the output of the system yðtÞ, i.e., eðtÞ ¼ rðtÞ yðtÞ
ð4:7-5Þ
The steady-state error ess ðtÞ is given by ess ðtÞ ¼ rss ðtÞ yss ðtÞ
ð4:7-6Þ
where ess ðtÞ ¼ lim eðtÞ; t!1
rss ðtÞ ¼ lim rðtÞ;
and
t!1
yss ðtÞ ¼ lim yðtÞ t!1
Clearly, the above definitions may be applied to nonunity feedback systems, as long as their equivalent block diagram of Figure 4.13b is used. The steady-state error (4.7-6) indicates the deviation of yss ðtÞ from rss ðtÞ. In practice, we wish ess ðtÞ ¼ 0, i.e., we wish yss ðtÞ to follow exactly the command signal rss ðtÞ. In cases where ess ðtÞ 6¼ 0, one seeks ways to reduce or even to zero the steadystate error. In order to evaluate ess ðtÞ, we work as follows. From Figure 4.12 we have EðsÞ ¼
RðsÞ 1 þ GðsÞ
ð4:7-7Þ
It is noted that for the general case of nonunity feedback systems, using Figure 4.13, relation (4.7-7) becomes " # 1 1 þ G~ ðsÞF~ ðsÞ G~ ðsÞ EðsÞ ¼ RðsÞ ¼ RðsÞ ð4:7-8Þ 1 þ GðsÞ 1 þ G~ ðsÞF~ ðsÞ If we apply the final value theorem (see relation (2.3-17)) to Eq. (4.7-7) we have ess ðtÞ ¼ lim sEðsÞ ¼ lim s!0
s!0 1
sRðsÞ þ GðsÞ
ð4:7-9Þ
given that the function sEðsÞ has all its poles on the left-half complex plane. We will examine the steady-state error ess ðtÞ for the following three special forms of the input r(t). 1. rðtÞ ¼ P. In this case the input is a step function with amplitude P. Here, ess ðtÞ is called the position error. We have ess ðtÞ ¼ lim sEðsÞ ¼ lim s!0
2
P ¼ 41 þ K 0
s!0 1
s½P=s P P ¼ ¼ þ GðsÞ 1 þ lim GðsÞ 1 þ Kp s!0
when j ¼ 0 when j > 0
ð4:7-10Þ
where use was made of relation (4.7-2). From relation (4.7-10) we observe that for type 0 systems the position error is P=ð1 þ KÞ, while for type greater than 0 systems the position error is zero.
174
Chapter 4
2. rðtÞ ¼ Vt. In this case the input is a ramp function with Here ess ðtÞ is called the speed or velocity error. We have 2 1 2 6A s½V=s V V ¼ ¼ ¼6 ess ðtÞ ¼ lim sEðsÞ ¼ lim s!0 s!0 1 þ GðsÞ lim sGðsÞ Kv 4 Kv s!0 0
slope equal to V. when j ¼ 0 when j ¼ 1 when j > 1 ð4:7-11Þ
where use was made of relation (4.7-3). From relation (4.7-11) we observe that for type 0 systems the speed error is infinity, for type 1 systems it is V=K, and for type greater than 1 systems it is zero. 3. rðtÞ ¼ ð1=2ÞAt2 . In this case the input is a parabolic function. Here, ess ðtÞ is called the acceleration error. We have 2 1 when j ¼ 0; 1 3 s½A=s A A 6A ¼ ess ðtÞ ¼ lim sEðsÞ ¼ lim ¼4 ¼ when j ¼ 2 s!0 s!0 1 þ GðsÞ K lim s2 GðsÞ Ka s!0 0 when j > 2 ð4:7-12Þ where use was made of relation (4.7-4). From relation (4.7-12) we observe that for type 0 and 1 systems the acceleration error is infinity, for type 2 systems it is A=K, and for type higher than 2 systems it is zero. In Figure 4.14 we present the error constants and the value of ess ðtÞ for type 0, 1, and 2 systems. Example 4.7.1 Consider the liquid-level control system of Subsec. 3.13.4 (Figure 3.53). For simplicity, assume that Ka Kv R K ; K ¼ Ka Kv Kf R GðsÞFðsÞ ¼ K ¼ RCs þ 1 f RCs þ 1 Determine yðtÞ and the steady-state error ess ðtÞ when the input rðtÞ is the unit step function. Solution We have
K=Kf YðsÞ ¼ HðsÞRðsÞ ¼ RCs þ 1 þ K
1 s
Thus, yðtÞ ¼ L1 fYðsÞg will be yðtÞ ¼
K ð1 et=T Þ; Kf ð1 þ KÞ
where
T¼
RC 1þK
The waveform of yðtÞ is given in Figure 4.15, from which it is obvious that the liquidlevel control system makes an attempt to ‘‘follow’’ the command signal rðtÞ ¼ 1. Unfortuantely, at the steady state it presents an error. To determine the steady-state error ess we work as follows. First, we convert the nonunity feedback system to unity feedback, according to Figure 4.13a. Since
Time-Domain Analysis
Figure 4.14
175
Position, speed, and acceleration errors.
K K R G~ ðsÞ ¼ a v RCs þ 1
and
F~ ðsÞ ¼ Kf
it follows that GðsÞ ¼
G~ ðsÞ K=Kf ¼ 1 þ G~ ðsÞF~ ðsÞ G~ ðsÞ RCs þ 1 þ K K=Kf
We have lim GðsÞ ¼
s!0
K=Kf 1 þ K K=Kf
Now, we are in position to apply relation (4.7-10) to yield ess ¼ lim sEðsÞ ¼ s!0
1 K þ KKf K ¼ f 1 þ lim GðsÞ Kf þ KKf s!0
To check the above results, we first determine yss ðtÞ ¼ lim sYðsÞ ¼ s!0
K Kf ð1 þ KÞ
176
Chapter 4
Figure 4.15
Time response of the closed-loop liquid-level control system when rðtÞ ¼ 1.
Therefore, the level yðtÞ of the liquid will never reach the desired level yðtÞ ¼ rðtÞ ¼ 1, but it will remain at a level lower than 1. The distance (i.e., the error) of this lower level to the desired level of 1 may be determined as follows: ess ¼ rss ðtÞ yss ðtÞ ¼ 1
K K þ Kf K K ¼ f Kf ð1 þ KÞ Kf ð1 þ KÞ
This error is in complete agreement with the steady-state error ess found above. It is remarked that this steady-state error may be eliminated if a more complex feedback transfer function FðsÞ is used, which would include not only the output analog feedback term Kf yðtÞ but also other terms – for example of the form Kd yð1Þ ðtÞ, i.e., terms involving the derivative of the output. Such feedback controllers, and even more complex ones, are presented in Chap. 9. Example 4.7.2 Consider the voltage control system of Subsec. 3.13.1 (Figure 3.50). For simplicity, let Tf ¼ Lf =Rf ¼ 2 and Kp ¼ 1; in which case, GðsÞ simplifies as follows: GðsÞ ¼
K 2s þ 1
Investigate the system’s steady-state errors. Solution If we apply the results of Subsecs 4.7.1 and 4.7.2 we readily have that, since the system is type 0, the error constants will be Kp ¼ K, Kv ¼ 0, and Ka ¼ 0. The steadystate error will be 2 P rðtÞ ¼ P 6 K ess ðtÞ ¼ 4 1 þ 1 rðtÞ ¼ Vt 1
rðtÞ ¼ 12 At2
Time-Domain Analysis
177
Example 4.7.3 Consider the position servomechanism of Subsec. 3.13.2 (Figure 3.51). Assume that La ’ 0. Then the open-loop transfer function reduces to GðsÞ ¼
Ka Ki N K K s þ R B þ K K ¼ sðAs þ BÞ ¼ sðs þ 2Þ s½Ra Jm a m i b
where we further have chosen Kp ¼ 1, A ¼ 1, B ¼ 2, and where K¼
Ka Ki N ; Ra
A ¼ Jm ;
and
B ¼ B m þ Ki Kb =Ra
This simplified system is shown in Figure 4.16. Here, eðtÞ ¼ e ðtÞ ¼ r ðtÞ y ðtÞ. Investigate the steady-state errors of the system. Solution If we apply the relations (4.7-2), (4.7-3), and (4.7-4), we readily have that, since the system is of type 1, the error constants will be Kp ¼ 1, Kv ¼ K=2, and Ka ¼ 0. The steady-state error will be 2 ð4:7-13aÞ 0 when r ðtÞ ¼ P 6 2V ð4:7-13bÞ when r ðtÞ ¼ Vt ess ðtÞ ¼ 4 K 2 1 1 when r ðtÞ ¼ 2 At ð4:7-13cÞ
4.8
DISTURBANCE REJECTION
In Subsec. 4.5.3, the effect of disturbances upon the output of open- and closed-loop systems is compared. In this section, an approach will be given which aims to reduce or even completely eliminate the effect of disturbances upon the system’s output. Consider the closed-loop system of Figure 4.17, involving the disturbance signal dðtÞ (or DðsÞ). This disturbance is usually external to systems under control. For example, a sudden change in the wind is an external disturbance for a microwave antenna mounted on the Earth. It may also be internal, e.g., an unexpected variation in the value of the capacitance C of a capacitor which is part of the system under control. Disturbances appear very often in practice, and they affect the system’s output, resulting in a deviation from its normal operating performance. The elimination of
Figure 4.16
Simplified block diagram of the position control system.
178
Chapter 4
the influence of dðtÞ on yðtÞ is the well-known problem of disturbance rejection and has, for obvious reasons, great practical importance. Using the general layout of Figure 4.17, we distinguish two cases: the unity feedback and the nonunity feedback systems. 1 Unity Feedback Systems In this case FðsÞ ¼ 1 and YðsÞ ¼ Gc ðsÞGðsÞEðsÞ þ GðsÞDðsÞ Furthermore, YðsÞ ¼ RðsÞ EðsÞ Eliminating YðsÞ in the above two equations, we have 1 GðsÞ EðsÞ ¼ Er ðsÞ þ Ed ðsÞ ¼ RðsÞ DðsÞ 1 þ Gc ðsÞGðsÞ 1 þ Gc ðsÞGðsÞ Hence, the steady-state error ess ðtÞ ¼ lim sEðsÞ is given by s!0 s sGðsÞ ess ðtÞ ¼ lim RðsÞ lim DðsÞ s!0 1 þ Gc ðsÞGðsÞ s!0 1 þ Gc ðsÞGðsÞ
ð4:8-1Þ
ð4:8-2Þ
ð4:8-3Þ
2 Nonunity Feedback Systems In this case FðsÞ 6¼ 1 and one may readily show that Gc ðsÞGðsÞ GðsÞ EðsÞ ¼ Er ðsÞ þ Ed ðsÞ ¼ 1 RðsÞ DðsÞ 1 þ Gc ðsÞGðsÞFðsÞ 1 þ Gc ðsÞGðsÞFðsÞ ð4:8-4Þ Hence, the steady-state error ess ðtÞ ¼ lim sEðsÞ is given by s!0 sGc ðsÞGðsÞ sGðsÞ ess ðtÞ ¼ lim s RðsÞ lim DðsÞ s!0 s!0 1 þ Gc ðsÞGðsÞFðsÞ 1 þ Gc ðsÞGðsÞFðsÞ
Figure 4.17
Closed-loop system with disturbances.
ð4:8-5Þ
Time-Domain Analysis
179
In the examples that follow, we show how to choose the controller Gc ðsÞ such as as to eliminate the influence of dðtÞ on yðtÞ in the steady state. Example 4.8.1 In this example we study the operation of an industrial company from the point of view of control system theory. In Figure 4.18, a simplified description of an industrial company is given, where it is shown that a company is run (or should be run) as a closed-loop control system, i.e., using the principle of feedback action. The process of producing a particular industrial product requires a certain amount of time. The time constant of the process is 1=a and, for simplicity, let a ¼ 1. The company’s board of directors (which here acts as the ‘‘controller’’ of the company) study in depth the undesirable error EðsÞ ¼ RðsÞ YðsÞ, where RðsÞ is the desired productivity and, subsequently, take certain appropriate actions or decisions. These actions may be approximately described by an integral controller Gc ðsÞ ¼ K1 =s, which integrates (smooths out) the error EðsÞ. The parameters K1 and K2 in the block diagram represent the effort put in by the management and by the production line, respectively. Investigate the steady-state errors for DðsÞ ¼ 0 and DðsÞ ¼ 1=s. The disturbance DðsÞ ¼ 1=s may be, for example, a sudden increase or decrease in the demand for the product. Solution (a) For the case DðsÞ ¼ 0, we have K1 K2 K ; where K ¼ K1 K2 ¼ sðs þ 1Þ sðs þ 1Þ 1 sðs þ 1Þ EðsÞ ¼ Er ðsÞ ¼ RðsÞ RðsÞ ¼ 2 1 þ Gc ðsÞGðsÞ s þsþK
Gc ðsÞGðsÞ ¼
By applying the results of Subsecs 4.7.1 and 4.7.2, we derive that, since it is a system of type 1, the error constants will be Kp ¼ 1, Kv ¼ 2K, and Ka ¼ 0. The steady state error is 2 0 when rðtÞ ¼ P when rðtÞ ¼ Vt ess ðtÞ ¼ 4 V=K 1 when rðtÞ ¼ 12 At2
Figure 4.18
Simplified closed-loop block diagram of a company.
180
Chapter 4
(b) For the case DðsÞ ¼ 1=s the error is given by the relation (4.8-2), i.e., by the relation EðsÞ ¼
RðsÞ GðsÞ DðsÞ ¼ Er ðsÞ þ Ed ðsÞ 1 þ Gc ðsÞGðsÞ 1 þ Gc ðsÞGðsÞ
With regard to Er ðsÞ, the results of case (a) remain the same. With regard to Ed ðsÞ, we have K sðs þ 1Þ 1 Ed ðsÞ ¼ 2 2 s þsþK s Hence, the steady-state error due to the disturbance DðsÞ will be lim ed ðtÞ ¼ lim sEd ðsÞ ¼ 0
t!1
s!0
Therefore, the effect of the disturbance on the steady-state error is zero. It is noted that for the closed-loop system to be stable there must be K > 0. Example 4.8.2 Consider the block diagram of Figure 4.19, where dðtÞ ¼ AðtÞ and rðtÞ ¼ 1 for t 0 and rðtÞ ¼ 0 for t < 0. Find the range of values of K1 and K2 such that the effect of the disturbance dðtÞ on the system’s output is eliminated (rejected) in the steady state, while simultaneously the system’s output follows the input signal, i.e., lim ½ðyðtÞ rðtÞ ¼ 0Þ. t!1
Solution To determine the system’s output we will apply the superposition principle. To this end, assume that the disturbance dðtÞ is the only input, in which case we get " # K2 ðs2 þ s þ 1Þ Yd ðsÞ ¼ DðsÞ; where DðsÞ ¼ LfdðtÞg ¼ A sðs2 þ s þ 1Þ þ K1 K2 ðs þ 1Þ Similarly, assuming that rðtÞ is the only input, we get K2 ðs þ 1Þ 1 RðsÞ; where RðsÞ ¼ LfuðtÞg ¼ Yr ðsÞ ¼ 2 s sðs þ s þ 1Þ þ K1 K2 ðs þ 1Þ Using the superposition principle, we have " # " # K2 ðs2 þ s þ 1Þ K2 ðs þ 1Þ 1 YðsÞ ¼ Aþ sðs2 þ s þ 1Þ þ K1 K2 ðs þ 1Þ sðs2 þ s þ 1Þ þ K1 K2 ðs þ 1Þ s For the disturbance dðtÞ to be eliminated in the steady-state, the following condition must hold: lim yd ðtÞ ¼ 0
t!1
To investigate the above condition, we use the final value theorem which, as is well known, holds if sYd ðsÞ is stable. For sYd ðsÞ to be stable, its denominator must not have any roots in the right-half complex plane. Using the Routh criterion (see Chap. 6) for the characteristic polynomial of the closed-loop system:
Time-Domain Analysis
Figure 4.19
181
System with input rðtÞ and disturbance dðtÞ.
pðsÞ ¼ s3 þ s2 þ ðK1 K2 þ 1Þs þ K1 K2 we form the Routh table K1 K2 þ 1 s3 1 K1 K2 s2 1 0 s1 1 0 s0 K 1 K 2 Hence, in order for pðsÞ to be stable, the condition K1 K2 > 0 must hold. Given that this condition holds and using the final value theorem, we obtain " # sK2 ðs2 þ s þ 1Þ lim yd ðtÞ ¼ lim sYd ðsÞ ¼ lim 3 A¼0 t!1 s!0 s!0 s þ s2 þ ðK1 K2 þ 1Þs þ K1 K2 Therefore, the effect of the disturbance in the steady state is eliminated when K1 K2 > 0. We will now examine the second condition lim ½ yðtÞ rðtÞ ¼ 0. Given that t!1 K1 K2 > 0, we have that sYðsÞ is stable. Hence, using the final value theorem and given that yd ðtÞ ¼ 0, in the steady state, we have that lim ½ yðtÞ rðtÞ ¼ lim½sYðsÞ sRðsÞ ¼ lim½sYr ðsÞ 1 s!0 s!0 K2 ðs þ 1Þ ¼ lim 3 1 ¼0 s!0 s þ s2 þ ðK1 K2 þ 1Þs þ K1 K2
t!1
or K2 1¼0 K1 K2 The above relation yields K1 ¼ 1 which, in conjunction with the condition K1 K2 > 0, gives the range of values of K1 and K2 , which are K1 ¼ 1
and
K2 > 0
Another approach to solve the problem is to use Eq. (4.8-4), in which case EðsÞ ¼ Er ðsÞ þ Ed ðsÞ where
182
Chapter 4
Gc ðsÞGðsÞ K2 ðs þ 1Þ Er ðsÞ ¼ 1 RðsÞ RðsÞ ¼ 1 2 1 þ Gc ðsÞGðsÞFðsÞ sðs þ s þ 1Þ þ K1 K2 ðs þ 1Þ GðsÞ K2 ðs þ 1Þ Ed ðsÞ ¼ DðsÞ DðsÞ ¼ 1 þ Gc ðsÞGðsÞFðsÞ sðs2 þ s þ 1Þ þ K1 K2 ðs þ 1Þ Here, RðsÞ ¼ 1=s and DðsÞ ¼ A and, hence, ess ¼ lim sEr ðsÞ þ lim sEd ðsÞ s!0
s!0
Simple calculations yield sK2 ðs þ 1Þ 1 K2 K 1 ¼1 ¼ 1 lim sEr ðsÞ ¼ lim s 2 s!0 s!0 K1 K1 K2 sðs þ s þ 1Þ þ K1 K2 ðs þ 1Þ s sK2 ðs þ 1Þ lim sEd ðsÞ ¼ lim A¼0 s!0 s!0 sðs2 þ s þ 1Þ þ K1 K2 ðs þ 1Þ Hence, the problem requirements are satisfied when K1 ¼ 1. However, for the above limits to exist, according to the final value theorem, the characteristic polynomial of the closed-loop system must be stable. This, as shown previously, leads to the condition K1 K2 > 0. Finally, we arrive at the conclusion that K1 and K2 must satisfy the conditions K1 ¼ 1 and K2 > 0, which are in perfect agreement with the results of the previous approach. Example 4.8.3 Consider a telephone network of signal transmission in which noise (disturbance) is introduced as shown in Figure 4.20. In the feedback path introduce a filter FðsÞ (FðsÞ is a rational function of s) such that for the closed-loop system in the steady state, the following conditions hold simultaneously: (a) The effect of the noises d1 ðtÞ and d2 ðtÞ on the output is rejected. (b) The receiver’s signal yðtÞ is the same as that of the transmitter’s signal rðtÞ. Solution First, we determine the system’s output due to the signals rðtÞ, d1 ðtÞ, and d2 ðtÞ, one at a time, using the superposition principle. When rðtÞ is the only input, we have G1 ðsÞG2 ðsÞ 1 Yr ðsÞ ¼ RðsÞ RðsÞ ¼ 1 G1 ðsÞG2 ðsÞFðsÞ ðs þ 2Þðs2 þ s þ 1Þ FðsÞ When the disturbance d1 ðtÞ is the only input, we have G2 ðsÞ sþ2 Yd1 ðsÞ ¼ D1 ðsÞ ¼ 2 1 G1 ðsÞG2 ðsÞFðsÞ ðs þ 2Þðs þ s þ 1Þ FðsÞ When the disturbance d2 ðtÞ is the only input, we have G2 ðsÞG1 ðsÞ 1 Yd2 ðsÞ ¼ D2 ðsÞ ¼ 2 1 G1 ðsÞG2 ðsÞFðsÞ ðs þ 2Þðs þ s þ 1Þ FðsÞ According to the problem’s requirements, the following must be simultaneously valid
Time-Domain Analysis
Figure 4.20
183
Telephone network with transmission noise.
lim yd1 ðtÞ ¼ lim yd2 ðtÞ ¼ 0
t!1
t!1
lim ½ yðtÞ rðtÞ ¼ 0;
t!1
for every rðtÞ
From requirement (a) of the problem, and by choosing FðsÞ such that the denominator ðs þ 2Þðs2 þ s þ 1Þ FðsÞ is stable, we must have lim sYd1 ðsÞ ¼ lim sYd2 ðsÞ ¼ 0
s!0
s!0
From requirement (b) of the problem, we have lim s½YðsÞ RðsÞ ¼ lim s½Yr ðsÞ RðsÞ ¼ 0;
s!0
s!0
for every RðsÞ
under the assumption that the effect of the disturbances has been eliminated. The above relation may be written as follows 1 lim s 1 RðsÞ ¼ 0: s!0 ðs þ 2Þðs2 þ s þ 1Þ FðsÞ This relation must hold for every RðsÞ. Consequently, the following must hold: 1 ¼1 ðs þ 2Þðs þ s þ 1Þ FðsÞ 2
The above relation yields FðsÞ ¼ ðs þ 1Þ3 . By replacing FðsÞ ¼ ðs þ 1Þ3 in Yd1 ðsÞ and Yd2 ðsÞ we obtain Yd1 ðsÞ ¼ s þ 2
and
Yd2 ðsÞ ¼ 1
Using these values, one may readily prove that the following condition is satisfied: lim sYd1 ðsÞ ¼ lim sYd2 ðsÞ ¼ 0
s!0
s!0
Hence, the function FðsÞ ¼ ðs þ 1Þ3 is the transfer function sought. To realize the function FðsÞ ¼ ðs þ 1Þ3 , one may use Figure 9.40. From this figure it follows that the gain of the operational amplifier is given by
184
Chapter 4
GðsÞ ¼
Z2 ðsÞ ¼ ðs þ 1Þ Z1 ðsÞ
where use was made of the PD controller case and where the values of R1 , R2 and C1 are chosen as follows: R1 ¼ R2 ¼ 1=C1 . If we have in cascade three such operational amplifiers together with an inverter, then the total transfer function Gt ðsÞ will be Gt ðsÞ ¼ GðsÞGðsÞGðsÞð1Þ ¼ ðs þ 1Þ3 in which case FðsÞ ¼ Gt ðsÞ ¼ ðs þ 1Þ3 .
PROBLEMS 1. Find the currents i1 ðtÞ and i2 ðtÞ for the network shown in Figure 4.21. Assume that the switch is closed at t ¼ 0 and the initial condition for the inductor current is i2 ð0Þ ¼ 0. 2. Find and plot the response yðtÞ of the network shown in Figure 4.22 for R ¼ 1 , 2 , 4 , and 10 . The input is uðtÞ ¼ 1. 3. Find and plot the response yðtÞ of the mechanical system shown in Figure 4.23 for B ¼ 1, 2, and 4. The input is uðtÞ ¼ 1. 4. The block diagram of the liquid-level control system of Figure 4.24a is shown in Figure 4.24b. The liquid flows into the tank through a valve that controls the input flow Q1 . When the output flow Q2 through the orifice increases, the liquidlevel height H decreases. As a result, the sensor that measures H causes the valve to open in order to increase the input flow Q1 . When the output flow Q2 decreases, H increases, and the valve closes in order to decrease the input flow Q1 . Determine the parameters K and T, given that for a step input the maximum overshoot is 25.4% and occurs when t1 ¼ 3 sec. 5. The block diagram of a system that controls the movement of a robot arm is given in Figure 4.25. For a unit step input, the system has a maximum percent overshoot of 20%, which occurs when t1 ¼ 1 sec. Determine: (a) The constants K and Kh (b) The rise time tr required for the output to reach value 1 for the first time (c) The settling time ts required for the output to reach and stay within 2% and 5% of its final value
Figure 4.21
Time-Domain Analysis
185
Figure 4.22
6. Consider the closed-loop system shown in Figure 4.12. Find the position, speed, and acceleration error constants when (a)
GðsÞ ¼
10 ðs þ 1Þð2s þ 1Þ
(b)
GðsÞ ¼
K sðs þ 1Þð2s þ 1Þ
ðcÞ GðsÞ ¼
K s ð0:5s þ 1Þðs þ 1Þ
ðdÞ GðsÞ ¼
2
Kðs þ 4Þ þ 6s þ 2Þ
s2 ðs2
7. For the closed-loop systems of Problem 6 find the steady-state position, speed, and acceleration errors. 8. The block diagram of an active suspension system for an automobile is shown in Figure 4.26. In this system, the position of the valves of the shock absorber is controller by means of a small electric motor: (a) Find the position, speed, and acceleration error constants. (b) Determine the steady-state position, speed, and acceleration errors. 9. The block diagram of a position servomechanism is shown in Figure 4.27. Determine the steady-state error when the input is rðtÞ ¼ a0 þ a1 t þ a2 t2 . 10. For the Example 4.7.2 find the error ess ðtÞ when rðtÞ ¼ 1 þ 2t t2 and when rðtÞ ¼ 10t3 .
Figure 4.23
186
Chapter 4
Figure 4.24
11. Find the position, speed, and acceleration error constants of all systems described in Sec. 3.13. 12. Find the steady-state position, speed, and acceleration errors of all systems described in Sec. 3.13. 13. A control system for a human heart with problems related to heart rate is shown in Figure 4.28. The controller used is an electronic pacemaker to keep the heart rate within a desired range. Determine a suitable transfer function for the pacemaker, so that the steady-state error due to a disturbance dðtÞ ¼ 1; t > 0, is zero.
Figure 4.25
Time-Domain Analysis
187
Figure 4.26
Figure 4.27
14. The block diagram of a position control system of a large microwave antenna is shown in Figure 4.29. To design such a system, we must take into account the disturbance due to large wind gust torques. Determine the range of values of K1 and K2 , so that the effect of the disturbance dðtÞ ¼ ðtÞ is minimized in the steady state, while the output follows the input signal rðtÞ ¼ 1.
Figure 4.28
188
Chapter 4
Figure 4.29
15. Consider a remotely controlled vehicle used for reconnaissance missions. The desired speed of the vehicle is transmitted to a receiver mounted on the vehicle. The block diagram of the control system is shown in Figure 4.30, where the disturbance input expresses the transmission noise. Find a transfer function for the feedback controller FðsÞ so that the effect of noise dðtÞ ¼ ðtÞ at the output is eliminated as t ! 1, while lim ½ yðtÞ rðtÞ ¼ 0 for every input rðtÞ. t!1 16. The block diagram of a system that controls the roll angle of a ship is shown in Figure 4.31. Determine the values of the gain Kp so that the disturbance due to wind is eliminated while the output follows a step input at steady state, when (a) Ki ¼ 0 (proportional controller) (b) Ki ¼ 1 (proportional plus integral controller) 17. For the system shown in Figure 4.32, determine K1 and K2 , so that the effect of noise dðtÞ ¼ ðtÞ is minimized at steady state. 18. Consider the system shown in Figure 4.33. Determine the transfer function of the controller Gc ðsÞ, so that when the disturbance is dðtÞ ¼ t, the steady-state error is zero.
Figure 4.30
Time-Domain Analysis
Figure 4.31
Figure 4.32
Figure 4.33
189
190
Chapter 4
BIBLIOGRAPHY Books 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35.
DK Anand. Introduction to Control Systems. New York: Pergamon Press, 1974. PJ Antsaklis, AN Michel. Linear Systems. New York: McGraw-Hill, 1997. DM Auslander, Y Takahasi, MJ Rabins. Introducing Systems and Control. New York: McGraw-Hill, 1974. RN Clark. Introduction to Automatic Control Systems. New York: John Wiley, 1962. JB Cruz Jr (ed). System Sensitivity Analysis. Strousdburg, Pennsylvania: Dowden, 1973. JJ D’Azzo, CH Houpis. Linear Control System Analysis and Design, Conventional and Modern. New York: McGraw-Hill, 1975. RA DeCarlo. Linear Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1989. BE DeRoy. Automatic Control Theory. New York: John Wiley, 1966. JJ DiStefano III, AR Stubberud, IJ Williams. Feedback and Control Systems. Schaum’s Outline Series. New York: McGraw-Hill, 1967. EO Doebelin. Dynamic Analysis and Feedback Control. New York: McGraw-Hill, 1962. RC Dorf, RE Bishop. Modern Control Analysis. London: Addison-Wesley, 1995. JC Doyle. Feedback Control Theory. New York: Macmillan, 1992. VW Eveleigh. Introduction to Control Systems Design. New York: McGraw-Hill, 1972. TE Fortman, KL Hitz. An Introduction to Linear Control Systems. New York: Marcel Dekker, 1977. GF Franklin, JD Powell, ML Workman. Digital Control of Dynamic Systems. 2nd ed. London: Addison-Wesley, 1990. RA Gabel, RA Roberts. Signals and Linear Systems. 3rd ed. New York: John Wiley, 1987. SC Gupta, L Hasdorff. Automatic Control. New York: John Wiley, 1970. IM Horowitz. Synthesis of Feedback Systems. New York: Academic Press, 1963. BC Kuo. Automatic Control Systems. London: Prentice Hall, 1995. IJ Nagrath, M Gopal. Control Systems Engineering. New Delhi: Wiley Eastern Limited, 1977. GC Newton Jr, LA Gould, JF Kaiser. Analytical Design of Linear Feedback Controls. New York: John Wiley, 1957. NS Nise. Control Systems Engineering. New York: Benjamin and Cummings, 1995. M Noton. Modern Control Engineering. New York: Pergamon Press, 1972. K Ogata. Modern Control Systems. London: Prentice Hall, 1997. CL Phillips, RD Harbor. Feedback Control Systems. 2nd ed. Englewood Cliffs, New Jersey: Prentice Hall, 1991. FH Raven. Automatic Control Engineering. 4th ed. New York: McGraw-Hill, 1987. CE Rohrs, JL Melsa, D Schultz. Linear Control Systems. New York: McGraw-Hill, 1993. IE Rubio. The Theory of Linear Systems. New York: Academic Press, 1971. WJ Rugh. Linear Systems Theory. 2nd ed. Englewood Cliffs, New Jersey: Prentice Hall, 1996. NK Sinha. Control Systems. New York: Holt, Rinehart & Winston, 1986. SM Shinners. Modern Control System Theory and Design. New York: Wiley, 1992. R Tomovic. Sensitivity Analysis of Dynamical Systems. New York: McGraw-Hill, 1963. JG Truxal. Introductory System Engineering. New York: McGraw-Hill, 1972. J Van de Vegte. Feedback Control Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1990. LA Zadeh, CA Desoer. Linear System Theory – the State State Space Approach. New York: McGraw-Hill, 1963.
Time-Domain Analysis
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Articles 36.
EJ Davison. A method for simplifying linear dynamic systems. IEE Trans. Automatic Control AC-11:93–101, 1966. 37. TC Hsia. On the simplification of linear systems. IEEE Trans Automatic Control AC17:372–374, 1972. 38. PN Paraskevopoulos. Techniques in model reduction for large scale systems. Control and Dynamic Systems: Advances in Theory and Applications (CT Leondes, ed.). New York: Academic Press, 20:165–193, 1986. 39. PN Paraskevopoulos, CA Tsonis, SG Tzafestas. Eigenvalue sensitviity of linear timeinvariant control systems with repeated eigenvalues. IEEE Trans Automatic Control 19:911–928, 1975.
5 State-Space Analysis of Control Systems
5.1
INTRODUCTION
The classical methods of studying control systems are mostly referred to singleinput–single-output (SISO) systems, which are described in the time domain by differential equations or by a scalar transfer function in the frequency domain (see Chap. 4). The modern methods of studying control systems are referred to the general category of multi-input–multi-ouput (MIMO) systems, which are described in the time domain by state equations (i.e., by a set of linear first-order differential equations) or by a transfer function matrix in the frequency domain. The modern approach of describing a system via the state equations model, compared with the classical models of Chap. 4, has the distinct characteristic of introducing a new concept to the system description—namely, the system’s state variables. The state variables give information about the internal structure of the system, which the classical methods do not. This information is of great significance to the study of the structure and properties of the system, as well as to the solution of high-performance control design problems, such as optimal control, adaptive control, robust control, and pole assignment. This chapter is devoted to linear, time-invariant systems having the following state-space form: ¼ AxðtÞ þ BuðtÞ xðtÞ ð5:1-1aÞ yðtÞ ¼ CxðtÞ þ DuðtÞ
ð5:1-1bÞ
xð0Þ ¼ x0
ð5:1-1cÞ
where xðtÞ is an n-dimensional state vector, uðtÞ is an m-dimensional input vector, and yðtÞ is a p-dimensional ouput vector. The matrices A, B, C, and D are timeinvariant, and their dimensions are n n, n m, p n, and p m, respectively. The initial conditions are at t ¼ 0 and they are given by Eq. (5.1-1c). The first objective of this chapter is the solution of Eqs (5.1-1). This will be ¼ AxðtÞ done in two steps. In Sec. 5.2 the solution of the homogeneous equation xðtÞ 193
194
Chapter 5
will be determined. Subsequently, in Sec. 5.3, the general solution of Eqs (5.1-1) will be derived. The next objective of this chapter is the state vector transformations and special forms of the state equations, presented in Sec. 5.4. Block diagrams and signalflow graphs are given in Sec. 5.5. The important topics of controllability and observability are presented in Sec. 5.6. Finally, the Kalman decomposition theorem is given in Sec. 5.7. 5.2 5.2.1
SOLUTION OF THE HOMOGENEOUS EQUATION Determination of the State Transition Matrix
Consider the dynamic part (5.2-1a) of the state equations (5.2-1), i.e., consider the first-order vector differential equation ¼ AxðtÞ þ BuðtÞ; xðtÞ xð0Þ ¼ x0 ð5:2-1Þ The homogeneous part of Eq. (5.2-1) is ¼ AxðtÞ; xðtÞ xð0Þ ¼ x0
ð5:2-2Þ
We introduce the following definition. Definition 5.2.1 The state transition matrix of Eq. (5.2-2) is an n n matrix, designated by rðtÞ, which satisfies the homogeneous equation (5.2-2), i.e., r_ ðtÞ ¼ ArðtÞ
ð5:2-3Þ
A rather simple method to solve the homogeneous equation (5.2-2) and simultaneously determine the state transition matrix rðtÞ is to assume the solution of (5.22) in a form of Taylor series, i.e., to assume that the state vector xðtÞ has the form xðtÞ ¼ e0 þ e1 t þ e2 t2 þ e3 t3 þ
ð5:2-4Þ
where e0 ; e1 ; e2 ; e3 ; . . . are n-dimensional constant unknown vectors. To determine these unknown vectors, we successively differentiate Eq. (5.2-4) and then evaluate the derivatives at t ¼ 0. Thus, the zero derivative of xðtÞ at t ¼ 0 is xð0Þ ¼ e0 . The first derivative of xðtÞ at t ¼ 0 is xð1Þ ð0Þ ¼ e1 . However, from Eq. (5.2-2) we have that xð1Þ ð0Þ ¼ Axð0Þ. Hence, e1 ¼ Axð0Þ. The second derivative of xðtÞ at t ¼ 0 is xð2Þ ð0Þ ¼ 2e2 . However, if we take the second derivative of Eq. (5.2-2), we will have that xð2Þ ðtÞ ¼ Axð1Þ ðtÞ ¼ A2 xðtÞ and, therefore, xð2Þ ð0Þ ¼ A2 xð0Þ ¼ 2e2 . Further application of this procedure yields the solution of Eq. (5.2-2) sought, having the Taylor series form 1 22 1 33 ð5:2-5Þ xðtÞ ¼ I þ At þ A t þ A t þ xð0Þ 2! 3! The power series in the bracket defines the matrix eAt , i.e., eAt ¼ I þ At þ
1 22 1 33 A t þ A t þ 2! 3!
ð5:2-6Þ
The above series converges for all square matrices A. Therefore, Eq. (5.2-5) takes on the form
State-Space Analysis
xðtÞ ¼ eAt xð0Þ
195
ð5:2-7Þ
Relation (5.2-7) is the solution of the homogeneous equation (5.2-2). From Eq. (5.2-6) one may readily derive that deAt ¼ AeAt dt that is, the matrix eAt satisfies Eq. (5.2-3). Hence, it follows that the state transition matrix rðtÞ is given by rðtÞ ¼ eAt
ð5:2-8Þ
Another popular method to solve the homogeneous equation (5.2-2) and determine the transition matrix rðtÞ is that of using the Laplace transform. To this end, apply the Laplace transform to (5.2-2) to yield sXðsÞ xð0Þ ¼ AXðsÞ Solving for XðsÞ, we have XðsÞ ¼ ðsI AÞ1 xð0Þ
ð5:2-9Þ
Using the inverse Laplace transform in (5.2-9), we obtain xðtÞ ¼ L1 fðsI AÞ1 gxð0Þ
ð5:2-10Þ
Comparing Eqs (5.2-7) and (5.2-10), we immediately have that rðtÞ ¼ eAt ¼ L1 fðsI AÞ1 g
ð5:2-11Þ
Remark 5.2.1 From the above results, it follows that the state transition matrix rðtÞ depends only upon the matrix A. The state vector xðtÞ describes the system’s free response— namely, the response of the system when it is excited only by its initial condition x0 (i.e., here uðtÞ ¼ 0). Furthermore, according to Eq. (5.2-7), rðtÞ completely defines the transition of the state vector xðtÞ, from its initial state xð0Þ to any new state xðtÞ. This is the reason why the matrix rðtÞ is called the state transition matrix. Remark 5.2.2 For the more general case, where the initial conditions are given for t ¼ t0 , xðtÞ ¼ rðt; t0 Þxðt0 Þ
ð5:2-12Þ
where rðt; t0 Þ ¼ rðt t0 Þ ¼ eAðtt0 Þ
ð5:2-13Þ
This can easily be proved if the Taylor series (5.2-4) is expanded about the arbitrary point t ¼ t0 . 5.2.2
Properties of the State Transition Matrix
The state transition mattrix rðtÞ has various properties. Some of them are useful for the material that follows and are presented in the next theorem.
196
Chapter 5
Theorem 5.2.1 The state transition matrix rðtÞ has the following properties: rð0Þ ¼ I
ð5:2-14aÞ
r1 ðtÞ ¼ rðtÞ
ð5:2-14bÞ
rðt2 t1 Þrðt1 t0 Þ ¼ rðt2 t0 Þ;
8 t0 ; t1 ; t2
½rðtÞk ¼ rðktÞ
ð5:2-14cÞ ð5:2-14dÞ
Proof If we set t ¼ 0 in Eq. (5.2-6) and subsequently use Eq. (5.2-8), we immediately have property (5.2-14a). If we multiply (5.2-8) from the left by eAt and from the right by r1 ðtÞ, we have eAt rðtÞr1 ðtÞ ¼ eAt eAt r1 ðtÞ Canceling out terms in the above equation, we immediately have property (5.2-14b). Property (5.2-14c) can be proved if we use Eq. (5.2-13), as follows: rðt2 t1 Þrðt1 t0 Þ ¼ eAðt2 t1 Þ eAðt1 t0 Þ ¼ eAðt2 t0 Þ ¼ rðt2 t0 Þ Finally, property (5.2-14d) can be proven as follows: At At Akt e ffl{zfflfflfflfflfflfflfflffl eAt ½rðtÞk ¼ e|fflfflfflfflfflfflfflffl ffl} ¼ e ¼ rðktÞ ktimes
5.2.3
Computation of the State Transition Matrix
For the computation of the matrix eAt , many methods have been proposed. We present the three most popular ones. First Method This method is based on Eq. (5.2-11), i.e., on the equation rðtÞ ¼ L1 fðsI AÞ1 g To apply this method, one must first compute the matrix ðsI AÞ1 and subsequently take its inverse Laplace transform. A method for computing the matrix ðsI AÞ1 is given by the following theorem. Theorem 5.2.2 (Leverrier’s algorithm) It holds that rðsÞ ¼ ðsI AÞ1 ¼
sn1 F1 þ sn2 F2 þ þ sFn1 þ Fn sn þ a1 sn1 þ þ an1 s þ an
ð5:2-15aÞ
where F1 ; F2 ; . . . ; Fn and a1 ; a2 ; . . . ; an are determined by the recursive equations
State-Space Analysis
197
F1 ¼ I
a1 ¼ trðAF1 Þ
F2 ¼ AF1 þ a1 I
a2 ¼ 12 trðAF2 Þ ð5:2-15bÞ
.. . 1 an ¼ trðAFn Þ n
.. . Fn ¼ AFn1 þ an1 I
Upon determining the matrix rðsÞ using Leverrier’s algorithm, we expand rðsÞ in partial fractions, by extending the results of Sec. 2.4, which hold for scalar functions, to the more general case of matrix functions, to yield rðsÞ ¼
n X i¼1
1 r s i i
where 1 ; 2 ; . . . ; n are the roots of the characteristic polynomial pðsÞ of matrix A, where pðsÞ ¼ jsI Aj ¼ sn þ a1 sn1 þ a2 sn2 þ þ an1 s þ an ¼
n Y ðs i Þ i¼1
and where ri are constant n n matrices. The matrices ri can be computed by extending Eq. (2.4-3) to the matrix case, to yield ri ¼ lim ðs i ÞrðsÞ;
i ¼ 1; 2; . . . ; n
s!i
If certain roots of the characteristic polynomial pðsÞ are repeated or complex conjugate pairs, then analogous results to the scalar functions case given in Sec. 2.4 hold for the present case of matrix functions. Clearly, for the case of distinct roots, we have rðtÞ ¼ L1 frðsÞg ¼ eAt ¼
n X
r i e i t
i¼1
Second Method This method takes place entirely in the time domain and is based on the diagonalization of the matrix A. Indeed, if the eigenvalues of matrix A are distinct, then the eigenvector matrix M (see relation (2.10-5)) diagonalizes matrix A, i.e., A can be transformed to a diagonal matrix , via the transformation matrix M as follows: , ¼ M1 AM. Matrix rðtÞ, under the transformation M, becomes rðtÞ ¼ eAt ¼ Me,t M1
ð5:2-16Þ
Since 2 6 e, t ¼ 6 4
e 1 t
0 it follows that
e 2 t
0 ..
.
e n t
3 7 7; 5
2 6 6 ,¼6 4
1
0
0 2
..
.
n
3 7 7 7 5
198
Chapter 5
2 6 rðtÞ ¼ eAt ¼ M6 4
e 1 t
e 2 t
0 ..
0
.
3 7 1 7M 5
ð5:2-17Þ
e n t
In the case where the matrix A has repeated eigenvalues, analogous results may be derived (see Sec. 2.10). Third Method This method, as in the case of the second method, takes place entirely in the time domain and is based on the expansion of eAt in a power series—namely, it is based on Eq. (5.2-6). This particular method does not require the determination of the eigenvalues 1 ; 2 ; . . . ; n of the matrix A, as compared to the first and second methods and is relatively easy to implement on a digital computer. 5.3
GENERAL SOLUTION OF THE STATE EQUATIONS
To determine the state vector xðtÞ and the output vector yðtÞ of the linear system (5.2-1), we start by solving the first-order vector differential equation (5.2-1a), i.e., the vector differential equation ¼ AxðtÞ þ BuðtÞ; xðtÞ xðt Þ ¼ x ð5:3-1Þ 0
0
In the previous section we found that the free response of Eq. (5.3-1), i.e., the ¼ AxðtÞ of Eq. (5.3-1), is given by solution xh ðtÞ of the homogeneous part xðtÞ where rðt t0 Þ ¼ eAðtt0 Þ
xh ðtÞ ¼ rðt t0 Þxðt0 Þ;
ð5:3-2Þ
To determine the forced response or particular solution xf ðtÞ of Eq. (5.3-1), we assume that xf ðtÞ has the form xf ðtÞ ¼ rðt t0 ÞqðtÞ;
with xf ðt0 Þ ¼ 0
ð5:3-3Þ
where qðtÞ is an n-dimensional unknown vector. Substitute Eq. (5.3-3) into Eq. (5.31) to yield ¼ Arðt t ÞqðtÞ þ BuðtÞ rðt t0 ÞqðtÞ þ rðt t0 Þ qðtÞ ð5:3-4Þ 0 Since rðt t0 Þ ¼ Arðt t0 Þ, Eq. (5.3-4) takes on the form ¼ Arðt t ÞqðtÞ þ BuðtÞ Arðt t ÞqðtÞ þ rðt t Þ qðtÞ 0
0
0
or ¼ BuðtÞ rðt t0 Þ qðtÞ Hence ¼ r1 ðt t ÞBuðtÞ qðtÞ 0 If we integrate Eq. (5.3-5) from t0 to t, we have ðt qðtÞ ¼ r1 ð t0 ÞBuðÞd t0
ð5:3-5Þ
State-Space Analysis
199
where qðt0 Þ ¼ r1 ðt0 t0 Þxf ðt0 Þ ¼ xf ðt0 Þ ¼ 0, where use was made of Eq. (5.3-3). Thus, the forced response of Eq. (5.3-1) will be ðt xf ðtÞ ¼ rðt t0 ÞqðtÞ ¼ rðt t0 Þ r1 ð t0 ÞBuðÞd ðt ¼
t0 1
rðt t0 Þr ð t0 ÞBuðÞd t0
or xf ðtÞ ¼
ðt
rðt ÞBuðÞd
t0
where use was made of properties (5.2-14b,c). Hence, the general solution of Eq. (5.3-1) will be ðt xðtÞ ¼ xh ðtÞ þ xf ðtÞ ¼ rðt t0 Þxðt0 Þ þ rðt ÞBuðÞd t0
The output vector yðtÞ of system (5.1-1) can now be easily determined using Eq. (5.3-6) to yield ðt yðtÞ ¼ CxðtÞ þ DuðtÞ ¼ Crðt t0 Þxðt0 Þ þ C rðt ÞBuðÞd þ DuðtÞ t0
ð5:3-7Þ Example 5.3.1 Consider the network of Figure 5.1a with initial conditions iL ð0Þ ¼ 1 and vc ð0Þ ¼ 0. The input, the state variables, and the output of the network are shown in Figure 5.1b. Determine: (a) A state-space description (b) The state transition matrix (c) The solution of the homogeneous equation (d) The general solution Solution (a) The loop equation is ðt di þ 3i þ 2 idt ¼ vðtÞ dt 0 The two state variables, which are given in Figure 5.1b, are defined as follows: ðt x1 ðtÞ ¼ iðtÞdt and x2 ðtÞ ¼ x 1 ðtÞ ¼ iL ðtÞ ¼ iðtÞ 0
Using the above definitions, the loop equation can be written in state space as follows: x_ 2 ðtÞ þ 3x2 ðtÞ þ 2x1 ðtÞ ¼ vðtÞ. This equation, combined with the equation x_ 1 ðtÞ ¼ x2 ðtÞ, yields the following state equations for the given network:
200
Chapter 5
Figure 5.1
(a) RLC network; (b) the input, the state variables, and the output of the net-
work.
0 x_ 1 ðtÞ ¼ 2 x_ 2 ðtÞ
1 3
x2 ðtÞ 0 uðtÞ þ 1 x2 ðtÞ
with initial condition vector xð0Þ ¼ x0 ¼ ½0; 1T and uðtÞ ¼ vðtÞ. (b) To determine the transition matrix rðtÞ, we apply the first method of Sec. 5.2, as follows: 3 2 sþ3 1 1 6 ðs þ 1Þðs þ 2Þ ðs þ 1Þðs þ 2Þ 7 s 1 7 ¼6 rðsÞ ¼ ðsI AÞ1 ¼ 5 4 2 s 2 sþ3 ðs þ 1Þðs þ 2Þ ðs þ 1Þðs þ 2Þ Hence
2et e2t rðtÞ ¼ L frðsÞg ¼ 2et þ 2e2t 1
et e2t et þ 2e2t
(c) The solution of the homogeneous equation is xh ðtÞ ¼ rðtÞxð0Þ or xh ðtÞ ¼ rðtÞ
0 et e2t ¼ 1 et þ 2e2t
State-Space Analysis
201
(d) The general solution of the state vector is ðt xðtÞ ¼ xh ðtÞ þ xf ðtÞ ¼ rðtÞ xð0Þ þ rðt ÞbuðÞ d 0
The forced response xf ðtÞ is determined as follows: # ðt ðt " eðtÞ e2ðtÞ rðt ÞbuðÞ d ¼ uðÞ d 0 0 eðtÞ þ 2e2ðtÞ 3 2 ðt ðt t 2t 2 e d e e d e 7 6 0 0 7 6 ¼6 7 ðt 5 4 t ð t 2t 2 e e d þ 2e e d " ¼
0 t
e ðe 1Þ t
0 1 2t 2t ðe 2e
1Þ
et ðet 1Þ þ e2t ðe2t 1Þ
#
"1 ¼
2
et þ 12 e2t
#
et e2t
Hence, the general solution xðtÞ ¼ xh ðtÞ þ xf ðtÞ is as follows: # # " " t 1 1 2t x1 ðtÞ ðe e2t Þ þ ð12 et þ 12 e2t 2 2e xðtÞ ¼ ¼ ¼ x2 ðtÞ ðet þ 2e2t Þ þ ðet e2t Þ e2t The output of the system is yðtÞ ¼ vR ðtÞ ¼ RiðtÞ ¼ Rx2 ðtÞ ¼ 3e2t Working in the s-domain, we should arrive at the same result. Indeed we have XðsÞ ¼ ðsI AÞ1 x0 þ ðsI AÞ1 bUðsÞ ¼ ðsI AÞ1 ½x0 þ bUðsÞ 8" # 2 3 9 0 = < 0 1 4 þ 15 ¼ ðsI AÞ : 1 ; s 3 2 2 1 3 sþ3 1 2 3 0 6 ðs þ 1Þðs þ 2Þ ðs þ 1Þðs þ 2Þ 7 6 sðs þ 2Þ 7 74 6 7 ¼6 7 s þ 15 ¼ 6 4 1 5 5 4 2 s s ðs þ 1Þðs þ 2Þ ðs þ 1Þðs þ 2Þ sþ2 and hence xðtÞ ¼ L1 fXðsÞg ¼
1 2
12 e2t e2t
Example 5.3.2 Consider the network of Figure 5.2a with zero initial conditions. The inputs, the state variables, and the outputs of the network are shown in Figure 5.2b. Determine: (a) The state equations of the network (b) The transition matrix and the output vector of the network, when R1 ¼ 0 , R2 ¼ 1:5 , C ¼ 1 F, L1 ¼ L2 ¼ 1 H, and v1 ðtÞ ¼ v2 ðtÞ ¼ 1 V.
202
Chapter 5
Figure 5.2
(a) A two-loop network; (b) the inputs, the state variables, and the outputs of
the network.
Solution (a) The two loop equations are di1 ¼ v1 ðtÞ dt di R1 ði1 þ i2 Þ þ vc þ L2 2 þ R2 i2 ¼ v1 ðtÞ v2 ðtÞ dt R1 ði1 þ i2 Þ þ vc þ L1
Furthermore, we have that C
dvc ¼ i1 þ i2 dt
Hence, the three first-order differential equations that describe the network are dvc 1 1 ¼ i1 þ i2 C C dt di1 1 R R 1 ¼ vc 1 i1 1 i2 þ v1 ðtÞ L1 L1 dt L1 L1 di2 1 R R þ R2 1 1 ¼ vc 1 i1 1 i2 þ v1 ðtÞ v2 ðtÞ L2 L2 L2 dt L2 L2 Note that i1 ¼ iL1 and i2 ¼ iL2 . Thus, the state equations of the network are
State-Space Analysis
203
x ¼ Ax þ Bu y ¼ Cx where
3 2 3 vc x1 ðtÞ x ¼ 4 x2 ðtÞ 5 ¼ 4 iL1 5; i L2 x3 ðtÞ 2
2 0
1 C R 1 L1
6 6 6 1 6 A ¼ 6 6 L1 6 4 1 R 1 L2 L2 " # 0 1 0 C¼ 0 0 1 (b) Substituting 2 0 A ¼ 4 1 1
u¼
1 C R 1 L1
3
7 7 7 7 7; 7 7 R1 þ R2 5 L2
v1 ðtÞ ; v2 ðtÞ
y¼ 2
0 6 1 6 6 B ¼ 6 L1 6 4 1 L2
i L1 i L2
0
3
7 0 7 7 7; 7 1 5 L2
the given values for each element, we have 3 2 3 2 1 1 0 0 s 0 0 5; B ¼ 41 0 5; sI A ¼ 4 1 0 1:5 1 1 1
jsI Aj ¼ s½sðs þ 1:5Þ þ s þ 1:5 þ s ¼ s3 þ 1:5s2 þ 2s þ 1:5 ¼ ðs þ 1Þðs2 þ 0:5s þ 1:5Þ Therefore 1 rðsÞ ¼ ðsI AÞ1 ¼ 2 ðs þ 1Þðs þ 0:5s þ 1:5Þ 2 3 sðs þ 1:5Þ s þ 1:5 s 6 7 2 1 5 4 ðs þ 1:5Þ s þ 1:5s þ 1 s Hence
1
s2 þ 1
2
3 ’11 ðtÞ ’12 ðtÞ ’13 ðtÞ rðtÞ ¼ L1 fðsI AÞ1 g ¼ 4 ’21 ðtÞ ’22 ðtÞ ’23 ðtÞ 5 ’31 ðtÞ ’32 ðtÞ ’33 ðtÞ
where ’11 ðtÞ ¼ 0:25et þ e0:25t ð1:25 cos 1:199t þ 0:052 sin 1:199tÞ ’12 ðtÞ ¼ 0:5et þ e0:25t ð0:5 cos 1:199t þ 0:99 sin 1:199tÞ ’13 ðtÞ ¼ 0:5et þ e0:25t ð0:5 cos 1:199t þ 0:521 sin 1:199tÞ ’21 ðtÞ ¼ ’12 ðtÞ ’22 ðtÞ ¼ 0:25et þ e0:25t ð0:75 cos 1:199t þ 0:365 sin 1:199tÞ
1 s 0
3 1 0 5 s þ 1:5
204
Chapter 5
’23 ðtÞ ¼ 0:5et þ e0:25t ð0:5 cos 1:199t 0:312 sin 1:199tÞ ’31 ðtÞ ¼ ’13 ðtÞ ’32 ðtÞ ¼ ’23 ðtÞ ’33 ðtÞ ¼ et 0:417e0:25t sin 1:199t The output vector is
" 1 s2 þ 1:5s YðsÞ ¼ ½CðsI AÞ1 BUðsÞ ¼ ðs þ 1Þðs2 þ 0:5s þ 1:5Þ s2 1=s
#
1 s2 1
1=s
" # 1 s2 þ 1:5s þ 1 ¼ sðs þ 1Þðs2 þ 0:5s þ 1:5Þ 1 Therefore
y1 ðtÞ yðtÞ ¼ L1 fYðsÞg ¼ y2 ðtÞ " # t 0:67 þ 0:08e þ 0:75e0:25t cos 1:2t 0:82e0:25t sin 1:199t ¼ 0:67 þ 0:17et þ 0:5e0:25t cos 1:2t þ 0:23e0:25t sin 1:199t
5.4
STATE VECTOR TRANSFORMATIONS AND SPECIAL FORMS OF STATE EQUATIONS
Consider the linear transformation x ¼ Tz
ð5:4-1Þ
of the state vector x of system (5.1-1), where T is the transformation matrix n n with jTj 6¼ 0 and z is the new n-dimensional state vector. Substitute Eq. (5.4-1) into Eq. (5.1-1) to yield T z ¼ ATz þ Bu y ¼ CTz þ Du xð0Þ ¼ Tzð0Þ or z ¼ A* z þ B* u
ð5:4-2aÞ
y ¼ C* z þ D* u
ð5:4-2bÞ
zð0Þ ¼ z0
ð5:4-2cÞ
where A* ¼ T1 AT
ð5:4-3aÞ
B* ¼ T1 B
ð5:4-3bÞ
State-Space Analysis
205
C* ¼ CT
ð5:4-3cÞ
D* ¼ D
ð5:4-3dÞ
zð0Þ ¼ T1 xð0Þ
ð5:4-3eÞ
For brevity, systems (5.1-1) and (5.4-2) are presented as ðA; B; C; DÞn and as ðA* ; B* ; C* ; D* Þn , respectively. System ðA* ; B* ; C* ; D* Þn is called the transformed state model of system ðA; B; C; DÞn . The motivation for transforming the state vector x to the state vector z is to select a new coordinate system z1 ; z2 ; . . . ; zn which has more advantages than the original coordinate system x1 ; x2 ; . . . ; xn . These advantages are related to the physical structure and characteristics as well as to the computational and technological aspects of the given system. Therefore, the problem of transforming x to z consists in determining a suitable state transformation matrix T such that the forms of A* ; B* ; C* ; and D* of the new system (5.4-2) have certain desirable characteristics. Usually, more attention is paid to matrix A* , so that its form is as simple as possible. The most popular such forms for A* are the diagonal and the phase canonical form. 5.4.1
The Invariance of the Characteristic Polynomial and of the Transfer Function Matrix
Independently of the particular choice of the transformation matrix T, certain characteristics of the initial system ðA; B; C; DÞn remain invariant under state transformation. Two such characteirstics are the characteristic polynomial and the transfer function matrix. This is proven in the following theorem. Theorem 5.4.1 It holds that pðsÞ ¼ p ðsÞ
ð5:4-4Þ
HðsÞ ¼ H ðsÞ
ð5:4-5Þ
where pðsÞ ¼ characteristic polynomial of matrix A ¼ jsI Aj p ðsÞ ¼ characteristic polynomial of matrix A* ¼ jsI A* j HðsÞ ¼ transfer function matrix of system (5.1-1) ¼ CðsI AÞ1 B þ D H* ðsÞ ¼ transfer function matrix of system (5.4-2)=C* ðsI A* Þ1 B* þ D* Proof We have p ðsÞ ¼ jsI A* j ¼ jsI T1 ATj ¼ jT1 ðsI AÞTj ¼ jT1 jjsI AjjTj ¼ jsI Aj ¼ pðsÞ Furthermore,
206
Chapter 5
H* ðsÞ ¼ C* ðsI A* Þ1 B* þ D* ¼ CTðsI T1 ATÞT1 B þ D ¼ CT½T1 ðsI AÞT1 T1 B þ D ¼ CTT1 ðsI AÞ1 TT1 B þ D ¼ CðsI AÞ1 B þ D ¼ HðsÞ where use was made of Eqs (5.4-3). Relations (5.4-5) have been also proved in Theorem 3.9.2. 5.4.2
Special State-Space Forms: The Phase Canonical Form
The main objective of the state vector transformation is to choose an appropriate matrix T such that the transformed system is as simple as possible. As mentioned earlier, one such simple form is the well-known phase canonical form. Another popular simple form is when the matrix A* is diagonal. Note that the problem of diagonalizing the matrix A of the original system is adequately covered in Sec. 2.10. For this reason we will not deal with the subject of diagonalization any further here. It is important to mention that diagonalization is a problem that is strongly related to the eigenvalues and eigenvectors of A, as compared with the phase canonical form, which is a problem strongly related to the controllability of the system (see Sec. 5.6). With regard to the phase canonical form, we give the following definitions. Definition 5.4.1 System ðA* ; B* ; C* ; D* Þn is in phase canonical form when the matrices A* and B* have the following special forms 2 * 3 B 1 7 6 2 * 3 6 7 A 11 A* 12 A* 1m 6 B* 7 6 2 7 6 A* 21 A* 22 A* 2m 7 7 6 7 6 7 ð5:4-6aÞ and B* ¼ 6 A* ¼ 6 . 7 . . 7 6 . . . 4 . . 5 . 6 .. 7 6 . 7 A* m1 A* m2 A* mm 7 6 45 B* m where
2
0 0 .. .
6 A* ii ¼ 6 4 " A* ij ¼ " B* i ¼
ða ii Þ0
1 0 .. .
ða ii Þ1
0 1 .. .
ða ii Þ2
0 0 .. .
ða ii Þi 1
0 ða ij Þ0
ða ij Þ1
ða ij Þ2
3 7 7 5 #
ða ij Þj 1
0 0
0
0
1
ðb i Þiþ1
ðb i Þiþ2
ð5:4-6bÞ
ð5:4-6cÞ #
ðb i Þm
" ith position where 1 ; 2 ; . . . ; m are positive integer numbers satisfying the relation
ð5:4-6dÞ
State-Space Analysis m X
207
i ¼ n
i¼1
Definition 5.4.2 Assume that system ðA* ; B* ; C* ; D* Þn has one input. In this case, the system is in its phase canonical form when the matrix A* and the vector b* have the special forms 3 2 2 3 0 1 0 0 0 0 1 0 7 607 6 0 6.7 6 . .. 7 .. .. . .7 and b* ¼ 6 ð5:4-7Þ A* ¼ 6 . 7 . . 7 6.7 6 . 5 5 4 0 4 0 0 1 0 a 0 a 1 a 2 a n1 1 It is obvious that Definition 5.4.2 constitutes a special (but very important) case of Definition 5.4.1. With regard to the procedure of transforming a system to its phase canonical form, we present the following theorems. Theorem 5.4.2 Assume that the system ðA; B; C; DÞn has one input and that the matrix . . . . S ¼ ½b .. Ab .. A2 b .. .. An1 b
ð5:4-8Þ
is regular, i.e., jSj 6¼ 0. In this case, there exists a transformation matrix T which transforms the given system to its phase canonical form ðA* ; B* ; C* ; D* Þn , where the matrix T is given by T ¼ P1 where 3 2 p1 67 6 6 7 6 6 p2 7 6 6 7 6 67 6 6 7 6 p 7 6 P¼6 6 37¼6 67 6 6 . 7 6 6 .. 7 6 6 7 6 45 4 2
pn
q qA qA2 .. .
3
7 7 7 7 7 7 7 7 7 7 7 7 5 qAn1
ð5:4-9Þ
where q is the last row of the matrix S1 . Proof Since x ¼ Tz, it follows that z ¼ T1 x ¼ Px, where P ¼ T1 . We also have z1 ¼ p1 x and z_1 ¼ p1 x. If we replace x ¼ Ax þ bu we have that z_1 ¼ p1 ðAx þ buÞ ¼ p1 Ax þ p1 bu. From the structure of the matrix A* given in Eq. (5.4-7), it follows that z_1 ¼ z2 ; z_2 ¼ z3 ; . . . ; z_n1 ¼ zn . Hence, the expression for z_1 takes on the form
208
Chapter 5
z_1 ¼ z2 ¼ p1 Ax þ p1 bu Since, according to relation z ¼ Px, the elements of z are functions of the elements of x only, it follows that p1 b ¼ 0. Also, since z_ ¼ z ¼ p A x ¼ p AðAx þ buÞ ¼ p A2 x þ p Abu 2
3
1
1
1
1
it follows that p1 Ab ¼ 0. If we repeat this procedure, we arrive at the final equation z_n1 ¼ zn ¼ p1 An2 x ¼ p1 An2 ðAx þ buÞ ¼ p1 An1 x þ p1 An2 bu and, consequently, p1 An2 b ¼ 0. The above results can be summarized as follows: z ¼ Px where
2
p1 p1 A p1 A 2 .. .
3
7 6 7 6 7 6 7 6 7 6 7 6 7 6 P¼6 7 7 6 7 6 7 6 7 6 4 5 p1 An1 where p1 is, for the time being, an arbitrary row vector which must satisfy the following relations: p1 b ¼ p1 Ab ¼ p1 A2 b ¼ ¼ p1 An2 b ¼ 0 or . . . . p1 ½b .. Ab .. A2 b .. .. An2 b ¼ 0 For the vector b* ¼ Pb to have the form (5.4-7), it must hold that 3 2 3 2 0 p1 b 67 67 7 6 7 6 6 p1 Ab 7 6 0 7 7 6 7 6 7 ¼ 67 b* ¼ Pb ¼ 6 7 6 . 7 6 . 7 6 .. 7 6 .. 7 6 7 6 45 45 1 p1 An1 b The above relation holds when p1 An1 b ¼ 1. Hence, the vector p1 must satisfy the equation . . . . . p1 ½b .. Ab .. A2 b .. .. An2 b .. An1 b ¼ p1 S ¼ ½0; 0; . . . ; 0; 1 Hence p1 ¼ ½0; 0; ; 0; 1S1 ¼ q where q is the last row of matrix S1 .
State-Space Analysis
209
Example 5.4.1 Consider a system of the form (5.1-1), where 1 1 1 A¼ ; b¼ 1 1 2 Transform matrix A and vector b to A* and b* of the form (5.4-7). Solution We have 2 .. S ¼ ½b . Ab ¼ 4 1 1
3 .. . 0 5; .. . 1
1
S
1 ¼ 1
0 1
Since S is invertible, matrix A and vector b can be transformed in phase canonical form. Matrix P will then be " P¼
q1
#
" ¼
q1 A
1
1
2
3
"
# ;
P
1
¼
3 1
#
2 1
and hence T ¼ P1 given above. Consequently, " A* ¼ T1 AT ¼ " 1
b* ¼ T b ¼
1
1
2
3
1
1
2
3
#"
1
1
1
2
#" # 1 1
¼
#"
3 1 2 1
#
" ¼
0
1
1
3
#
" # 0 1
Theorem 5.4.3 Assume that the system ðA; B; C; DÞn is a MIMO. Further, assume that there are positive integer numbers 1 ; 2 ; . . . ; m such that the matrix . . . . . . . . . S^ ¼ ½b1 .. Ab1 .. .. A1 1 b1 jb2 .. Ab2 .. .. A2 1 b2 j jbm .. Abm .. .. Am 1 bm ð5:4-10Þ is of full rank and that 1 þ 2 þ þ m ¼ n, where bi is the ith column of the matrix B (a systematic way of choosing the integers 1 ; 2 ; . . . ; m is given in [11]). Then, there is a transformation matrix T which transforms the given system to its phase canonical form ðA* ; B* ; C* ; D* Þn . Matrix T is given by the relation T ¼ P1 , where
210
Chapter 5
2 3
2
p1 p2 .. .
3
2
q1 q1 A .. .
3
7 6 7 6 7 6 7 6 7 7 6 6 6 P1 7 6 7 7 6 7 6 6 7 7 6 7 6 p 6 1 1 7 7 6 q A 7 6 6 1 1 7 7 6 6 7 67 6 7 7 6 6 7 7 6 7 6 p 6 7 6 7 q2 7 6 6 1 þ1 7 7 6 7 6 p 6 7 7 6 q A 7 6 6 2 þ2 7 2 7 6 6 P 7 6 7 6 7 . .. 6 2 7 6 . 7 7 6 7 6 6 . . 7¼6 7 P¼6 7¼6 2 1 7 7 6 7 6 p1 þ2 7 6 q2 A 6 7 7 6 6 7 6 6 7 67 7 67 7 6 6 . . 7 7 6 6 .. 7 6 .. .. 7 6 7 6 . 7 6 7 7 6 7 6 7 67 6 7 6 7 7 6 6 7 6 p 6 7 6 qm 7 6 nm 7 6 7 7 6 7 6 6 7 6 5 6 pnm þ1 7 6 qm A 7 4 7 7 6 7 6 .. .. Pm 5 5 4 4 . . m 1 A q pn m
ð5:4-11Þ
where qk is the k row of the matrix S^ 1 and where k ¼
k X
i
k ¼ 1; 2; . . . ; m
ð5:4-12Þ
i¼1
Example 5.4.2 Consider a system of the form (5.1-1), where 2 3 2 3 2 1 1 1 1 6 7 6 7 A¼4 3 B ¼ 4 1 1 1 5; 0 5; 1 1 5 1 3 0 0 D¼ 0 0
C¼
2
0
2
0
1
1
;
Transform this system to its phase canonical form (5.4-6). Solution We have S^ ¼ ½b1 2 6 1 S^ ¼ 6 4 1 1
.. . . Ab1 .. b2 . Hence 3 .. . . 0 .. 17 .. . 7 . 1 .. 05 .. .. . 1 . 1
2
and
S^ 1
1 ¼ 41 0
1 0 1
3 1 15 1
Here, 1 ¼ 2 and 2 ¼ 1 and thus 1 þ 2 ¼ 3 ¼ n. Also, 1 ¼ 1 ¼ 2 and 2 ¼ 1 þ 2 ¼ 3. The rows q1 and q2 are the 2nd and 3rd rows of the matrix S^ 1 , i.e., q1 ¼ ð1; 0; 1Þ and q2 ¼ ð0; 1; 1Þ. Therefore, the matrix P has the form
State-Space Analysis
211
3 2 1 0 q1 6 q1 A 7 6 2 0 7¼6 P¼6 5 4 4 0 1 q2 2
3 1 3 7 7 5 1
2
and
P1
2 ¼4 3 3
1 1 1
3 0 15 0
Finally, the matrices of the transformed system ðA* ; B* ; C* ; D* Þn have the form 2 3 2 3 0 1 0 * * A11 A12 6 7 2 2 1 7 4 5 A* ¼ T1 AT ¼ 6 4 5¼ * * A21 A22 2 0 0 3 2 2 3 0 0 B1* 7 6 1 1 1 7 4 5 6 * B ¼T B¼4 5¼ * B2 0 1 2 0 0 0 0 * * and D ¼D¼ C ¼ CT ¼ 0 0 1 0 0 We observe that the forms of A* and B* are in agreement with the forms in Eqs (5.4-6). 5.4.3
Transition from an nth Order Differential Equation to State Equations in Phase Canonical Form
Consider the differential equation yðnÞ þ an1 yðn1Þ þ an2 yðn2Þ þ þ a1 yð1Þ þ a0 y ¼ uðtÞ Define as state variables x1 ðtÞ ¼ yðtÞ; x2 ðtÞ ¼ yð1Þ ðtÞ; . . . ; xn ðtÞ ¼ yðn1Þ ðtÞ. This particular set of state variables are called phase variables. Then, as shown in Subsec. 3.8.5, the above differential equation in state space takes on the following form: x ¼ Ax þ bu;
y ¼ cT x
where A and b are in phase canonical form of the form (5.4-7). The vector cT has the form cT ¼ ð1; 0; . . . ; 0Þ. It is important to observe that the structure of matrix A in phase canonical form is as follows 2 3 0 In1 5 A¼4 a where 0 is a zero column of dimension n 1, the matrix In1 is the unity ðn 1Þ ðn 1Þ matrix and a ¼ ½a0 ; a1 ; a2 ; . . . ; an1 is a row whose elements are the coefficients of the differential equation, in reverse order and with negative sign. Due to this special structure of matrix A, we readily have the following three relations: pðsÞ ¼ jsI Aj ¼ sn þ an1 sn1 þ an2 sn2 þ þ a1 s þ a0
ð5:4-13aÞ
212
Chapter 5
2 ðsI AÞ1 b ¼
6 1 6 6 pðsÞ 6 4
3
1 s s2 .. .
7 7 7 7 5
ð5:4-13bÞ
1 pðsÞ
ð5:4-13cÞ
sn1 HðsÞ ¼ cT ðsI AÞ1 b ¼
5.4.4
Transition from the Phase Canonical Form to the Diagonal Form
Assume that a system is already in phase canonical form and that we want to diagonalize the matrix A of the system. A general method of diagonalizing any square matrix is given in Sec. 2.10. For the special case, where the matrix A is already in phase canonical form, the diagonalization is simple and may be carried out as follows: a similarity matrix T, which diagonalizes the matrix A, can be determined by letting , ¼ T1 AT or
32 1 t1 6 t2 7 6 0 6 76 6 t2 7 6 0 6 76 6 .. 76 .. 4 . 54 .
0 2 0 .. .
0 0 3 .. .
0
0
0
2
tn
T, ¼ AT;
or
0 0 0 .. .
n
3
where , ¼ diagfi g 2
7 6 7 6 7 6 7¼6 7 4 5
0 0 0 .. .
a0
1 0 0 .. .
a1
0 1 0 .. .
a2
ð5:4-14Þ
0 0 0 .. .
an1
32 t 3 1 7 t 76 27 76 6 7 6 t3 7 76 . 7 54 . 7 .5 tn
or t1 , ¼ t2 t2 , ¼ t3 .. . tn1 , ¼ tn tn , ¼ aT where a ¼ ½a0 ; a1 ; a2 ; . . . ; an1 and ti is the ith row of the matrix T. The above relations can be written as t2 ¼ t1 , t3 ¼ t2 , ¼ t1 ,2 .. . tn1 ¼ tn2 , ¼ t1 ,n2 tn ¼ tn1 , ¼ t1 ,n1 tn , ¼ aT These equations yield t1 ¼ ½1; 1; . . . ; 1. Hence, T has the following form
State-Space Analysis
2
t1 t2 .. .
3
2
213
t1 t1 , .. .
3
2
1 1 .. .
7 6 7 6 6 7 6 7 6 6 7 6 7 6 6 T¼6 7¼6 7¼6 7 6 7 6 6 n2 4 tn1 5 4 t1 , 5 4 1n2 tn t2 ,n1 1n1
1 2 .. .
n2 2 n1 2
1 n .. .
3
7 7 7 7 7 5 n2 n n1 n
ð5:4-15Þ
This particular form of T is known as the Vandermonde matrix, and it is always regular under the condition that the eigenvalues 1 ; 2 ; . . . ; n are distinct. Example 5.4.3 Diagonalize the following matrix 2 3 0 1 0 A ¼ 40 0 15 6 11 6 Solution The characteristic polynomial pðsÞ of matrix A is pðsÞ ¼ jsI Aj ¼ s3 6s2 11s 6 ¼ ðs 1Þðs 2Þðs 3Þ Hence, the eigenvalues of A are 2 3 2 1 1 1 1 7 6 6 T ¼ 4 1 2 3 5 ¼ 4 1 1 21 22 23 Therefore
2
1 , ¼ T AT ¼ 4 0 0 1
5.5
1, 2, and 3. The transformation matrix T will be 3 1 1 7 2 35 4 9
3 0 0 2 05 0 3
BLOCK DIAGRAMS AND SIGNAL-FLOW GRAPHS
MIMO systems can be described by block diagrams and signal-flow graphs in a similar way that SISO systems are described (see Secs. 3.10 and 3.11). Consider a MIMO system, which is described in state space by the equations x ¼ Ax þ Bu; xð0Þ ¼ x ð5:5-1aÞ 0
y ¼ Cx þ Du
ð5:5-1bÞ
This system can also be described in the s-domain as follows: sXðsÞ xð0Þ ¼ AXðsÞ þ BUðsÞ YðsÞ ¼ CXðsÞ þ DUðsÞ
ð5:5-2aÞ ð5:5-2bÞ
The block diagrams of Eqs (5.5-1) and (5.5-2) are given in Figures 5.3 and 5.4, respectively. The signal-flow graph of Eqs (5.5-2) is given in Figure 5.5. For the special case where the system is described by the nth order differential equation
214
Chapter 5
Figure 5.3
Block diagram of state equations in the time domain.
Figure 5.4
Block diagram of state equations in the frequency domain.
Figure 5.5
Signal-flow graph of state equations.
yðnÞ þ an1 yðn1Þ þ þ a1 yð1Þ þ a0 y ¼ uðtÞ with zero initial conditions yðkÞ ð0Þ; k ¼ 0; 1; . . . ; n 1; the signal-flow graph of the state equations of this differential equation can be constructed as follows. Let x1 ¼ y; x2 ¼ yð1Þ ; . . . ; xn ¼ yðn1Þ . Then, the differential equation becomes x_ 1 ¼ x2 x_ 2 ¼ x3 .. . x_ n ¼ a0 x1 a1 x2 an1 xn þ uðtÞ
State-Space Analysis
Figure 5.6
215
Signal-flow graph of state equations of a SISO system.
Take the Laplace transform to yield sX1 ðsÞ x1 ð0Þ ¼ X2 ðsÞ sX2 ðsÞ x2 ð0Þ ¼ X3 ðsÞ .. . sXn ðsÞ xn ð0Þ ¼ a0 X1 ðsÞ a1 X2 ðsÞ an1 Xn ðsÞ þ UðsÞ From the above relations we can easily construct the signal-flow graph given in Figure 5.6. The corresponding block diagram has already been presented in Figure 3.12. 5.6
CONTROLLABILITY AND OBSERVABILITY
The concepts of controllability and observability have been introduced by Kalman [24–30] and are of great theoretical and practical importance in modern control. For example, controllability and observability play an important role in solving several control problems, such as optimal control, adaptive control, pole assignment, etc. 5.6.1
State Vector Controllability
The concept of controllability is related to the state vector as well as to the output vector of a system. Simply speaking, we say that the state (or output) vector is controllable if a control vector uðtÞ can be found such that the state (or output) vector reaches a preassigned value in a finite period of time. If this is not possible – i.e., even if one state (or output) variable cannot be controlled (in which case we say that this variable is uncontrollable) – it follows that the whole system is uncontrollable. As an introductionary example, consider the following system: x_ 1 1 0 x1 0 ¼ þ u 0 2 x2 1 x_ 2
216
Chapter 5
From the first equation x_ 1 ¼ x1 , it is obvious that the state variable x1 is not a function of the input u. Therefore, the behavior of x1 cannot be affected by the input u, and hence the variable x1 is uncontrollable. On the contrary, from the second equation x_ 2 ¼ 2x2 þ u, it follows that the variable x2 is controllable since the input u affects x2 , and we can therefore select an input u such that x2 reaches any preassigned value in a finite period of time. The strict definition of state controllability of system (5.2-1) is the following. Definition 5.6.1 The vector xðtÞ of system (5.1-1) is completely controllable or simply controllable if there exists a piecewise continuous control function uðtÞ such as to drive xðtÞ from its initial condition xðt0 Þ to its final value xðtf Þ in a finite period of time ðtf t0 Þ 0. In Definition 5.6.1 the expression ‘‘a piecewise . . . uðtÞ’’ has the meaning that we do not put any limitation on the amplitude or on the energy of uðtÞ. The definition of controllability of xðtÞ gives a very good insight into the physical meaning of controllability, but it is not easy to apply in order to determine whether or not xðtÞ is controllable. To facilitate this problem we give two alternative theorems (criteria) that simplify the determination of the controllability of xðtÞ. Theorem 5.6.1 Assume that the matrix A of system (5.1-1) has distinct eigenvalues. Also assume that the transformation matrix T diagonalizes matrix A, in which case the diagonalized system will be z ¼ ,z þ B* u; where , ¼ diagf g ¼ diagf ; ; . . . ; g ð5:6-1Þ i
1
1
1
2
n
1
where z ¼ T x, , ¼ T AT and B* ¼ T B. Then, xðtÞ is controllable if no row of matrix B* is a zero row. Proof Equation (5.6-1) can be written as z_i ¼ i zi þ b* i u;
i ¼ 1; 2; . . . ; n
ð5:6-2Þ
where b*i is the ith row of matrix B* . From Eq. (5.6-2) it is obvious that the state variable zi of system (5.6-1) is controllable if at least one of the elements of the row b*i is not zero. Hence, all variables z1 ; z2 ; . . . ; zn are controllable if none of the rows of matrix B* are zero. Theorem 5.6.2 The state vector xðtÞ of Eq. (5.1-1a) is controllable if and only if RankS ¼ n;
. . . . where S ¼ ½B .. AB .. A2 B .. .. An1 B
where S is called the controllability matrix and has dimensions n mn. Proof The solution of Eq. (5.1-1a) is given by Eq. (5.3-6), i.e., by the equation
ð5:6-3Þ
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217
xðtÞ ¼ rðt t0 Þxðt0 Þ þ
ðt
rðt ÞBuðÞ d
ð5:6-4Þ
t0
To simplify the proof, let xðtf Þ ¼ 0. Then, solving Eq. (5.6-4) for xðt0 Þ gives ðt ð tf f xðt0 Þ ¼ r1 ðtf t0 Þ rðtf ÞBuðÞ d ¼ rðtf þ t0 þ tf ÞBuðÞ d ¼
t0
ð tf
t0
rðt0 ÞBuðÞ d
ð5:6-5Þ
t0
where use was made of Eq. (5.2-14b). From Cayley–Hamilton’s theorem, given in Eq. (2.11-6), we have that Ak ¼
n1 X ð k Þi Ai ;
kn
i¼0
Using this relation, the state transition matrix rðtÞ can be written as rðtÞ ¼ eAt ¼
n1 k X t k¼0
k!
Ak þ
where i ðtÞ ¼
1 kX n1 1 n1 X X X t n1 tk X ð k Þi Ai ¼ Ai ð k Þi ¼ i ðtÞAi ; k! k! i¼0 i¼0 i¼0 k¼n k¼0
1 X tk ð k Þi k! k¼0
Using the above expression for rðtÞ, the matrix rðt0 Þ can be written as rðt0 Þ ¼
n1 X
i ðt0 ÞAi
ð5:6-6Þ
i¼0
If we substitute Eq. (5.6-6) into Eq. (5.6-5), we have ð tf n1 n1 X X i A B i ðt0 ÞuðÞ d ¼ Ai Bqi xðt0 Þ ¼ i¼0
t0
i¼0
where the vector qi is defined as ð tf i ðt0 ÞuðÞ d qi ¼ t0
The above expression for xðt0 Þ can be written in compact matrix form, as follows xðt0 Þ ¼ Sq
ð5:6-7Þ
where . . . . S ¼ ½B .. AB .. A2 B .. .. An1 B
2 and
6 6 q¼6 4
q0 q1 .. .
3 7 7 7 5
qn1 Equation (5.6-7) is a system of n equations with n m unknowns. The problem at hand, i.e., the determination of an input vector uðtÞ such that xðtf Þ ¼ 0, has been reduced to that of solving system (5.6-7). From linear algebra, it is well known that
218
Chapter 5
for system (5.6-7) to have a solution, the rank of S must be equal to n, i.e., condition (5.6-3) must hold. Example 5.6.1 Determine if the state vector of the system x ¼ Ax þ bu is controllable, where 1 2 3 A¼ and b¼ 0 5 0 Solution Construct the matrix S: 2 .. S ¼ ½b . Ab ¼ 4 1 0
3 .. . 25 .. . 0
Since jSj ¼ 0, it follows that the rank of S is less than n ¼ 2. Hence, the state vector is not controllable. Example 5.6.2 Determine if the state vector of a SISO system in phase canonical form (5.4-7) is controllable. Solution Construct the matrix S* :
2
0 0 60 0 6 . . . 6. .. S* ¼ ½b* .. A* b* .. .. ðA* Þn1 b* ¼ 6 .. . 6 40 1 1 a n1
1 1 .. .
3
7 7 7 7 7 n1 5 n
where 1 ; 2 ; . . . ; n are linear combinations of the coefficients a 0 ; a 1 ; . . . ; a n1 of the characteristic polynomial jsI A j. Due to the lower-diagonal form of matrix S* , it immediately follows that jS* j ¼ 1. Hence, the state vector of the SISO system (5.47) in phase canonical form is always controllable. Remark 5.6.1 Example 5.6.2, in combination with Theorem 5.4.2, shows that, for SISO systems, the controllability of the state vector is the necessary and sufficient condition required to transform the system to its phase canonical form. Hence, if a SISO system is already in its phase canonical form, it follows that its state vector is controllable. 5.6.2
Output Vector Controllability
The controllability of the output vector is defined as follows. Definition 5.6.2 The output vector yðtÞ of system (5.1-1) is completely controllable or simply controllable if there exists a piecewise continuous control function uðtÞ, which will drive yðtÞ
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219
from its initial condition yðt0 Þ to its final value yðtf Þ, in a finite period of time ðtf t0 Þ 0. A simple criterion for determining the controllability of the output vector is given by the following theorem. Theorem 5.6.3 The output vector yðtÞ of system (5.1-1) is controllable if and only if RankQ ¼ p;
. . . . . where Q ¼ ½D .. CB .. CAB .. CA2 B .. .. CAn1 B
ð5:6-8Þ
where matrix Q has dimensions p ðn þ 1Þm. The proof of Theorem 5.6.3 is analogous to the proof of Theorem 5.6.2. Example 5.6.3 Consider a system of the form (5.1-1), where 1 0 1 0 1 ; B¼ ; C¼ A¼ 0 2 0 1 1
1 ; 0
D¼
1 0
1 0
Determine if the state and output vectors are controllable. Solution We have
2 .. S ¼ ½B . AB ¼ 4 1 0 0 1
.. . 1 .. . 0
3 05 2
Since RankS ¼ 2, it follows that the state vector is controllable. Furthermore, we have 2 3 .. .. .. .. Q ¼ ½D . CB . CAB ¼ 4 1 1 .. 1 1 .. 1 2 5 0 0 .. 1 0 .. 1 0 Since RankQ ¼ 2, it follows that the output vector is also controllable. 5.6.3
State Vector Observability
The concept of observability is related to the state variables of the system and it is dual to the concept of controllability (the concept of duality is explained in Remarks 5.6.2 and 5.6.3, which follow). Assume that we have available the input vector uðtÞ and the corresponding output vector yðtÞ of system (5.1-1) over a finite period of time. If, on the basis of these measurements of uðtÞ and yðtÞ, one can determine the vector of initial conditions xðt0 Þ, then we say that the system is observable. In case that this is not possible—i.e., even if one element of the vector of initial conditions xðt0 Þ cannot be determined—then we say that this element is unobservable, and as a result we say that the whole system is unobservable. As an introductory example, consider the following system: 1 0 x1 1 x x_ 1 ¼ þ u; y ¼ ½2 0 1 x2 0 2 x2 1 x_ 2
220
Chapter 5
The above description breaks down to the two differential equations x_ 1 ¼ x1 þ u and x_ 2 ¼ 2x2 þ u. Furthermore, the output is given by y ¼ 2x1 . Since x_ 1 ¼ x1 þ u – i.e., x1 is not a function of x2 but only a function of u, it follows that the output of the system is affected only by the state x1 and therefore the output does not involve any information regarding the state x2 . As a result, the determination of the initial condition x2 ðt0 Þ becomes impossible. Hence, the system at hand is unobservable. The strict definition of observability is as follows. Definition 5.6.3 The state vector xðtÞ of system (5.1-1) is observable in the time interval ½t0 ; tf if, knowing the input uðtÞ and the output yðtÞ for t 2 ½t0 ; tf , one can determine the initial condition vector xðt0 Þ. In the sequel, we present two alternative theorems (criteria) that simplify the procedure of determining the observability of xðtÞ. Theorem 5.6.4 Let system (5.1-1) have distinct eigenvalues. Furthermore, let the transformation matrix T be given by (5.4-15), in which case T diagonalizes matrix A. Then the diagonalized system is the following: z ¼ ,z þ B* u ð5:6-9aÞ y ¼ C* z þ Du 1
ð5:6-9bÞ 1
1
where z ¼ T x, , ¼ T AT, B* ¼ T B, and C* ¼ CT. Then, xðtÞ is observable if no column of matrix C* is a zero column. Proof The output Eq. (5.6-9b) can be written as y ¼ c*1 z1 þ c*2 z2 þ þ c*i zi þ þ c*n zn þ Du
ð5:6-10Þ
where c* i is the ith column of matrix C* . It is obvious that if one column, for example ci , of matrix C* , is zero then the corresponding state variable zi will not appear in the output yðtÞ. Consequently, we cannot, in this case, determine the initial condition zi ðt0 Þ. As a result, xðtÞ is unobservable. Remark 5.6.2 Theorems 5.6.1 and 5.6.4 are dual, in the sense that the role of rows of matrix B* for controllability play the columns of matrix C* for observability. Theorem 5.6.5 The state vector xðtÞ of system (5.1-1) is observable if and only if rankRT ¼ n;
. . . . where RT ¼ ½CT .. AT CT .. ðAT Þ2 CT .. .. ðAT Þn1 CT ð5:6-11Þ
where R is called the observability matrix and has dimensions n np.
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221
Proof The general solution of Eq. (5.2-1) is given by Eq. (5.3-7), i.e., by the equation ðt ð5:6-12Þ yðtÞ ¼ Crðt t0 Þxðt0 Þ þ C rðt ÞBuðÞ d þ DuðtÞ t0
To simplify the proof, let uðtÞ ¼ 0. Then, Eq. (5.6-12) becomes yðtÞ ¼ Crðt t0 Þxðt0 Þ If we use Eq. (5.6-6) in the above relation, we have " # " # n1 n1 X X i i yðtÞ ¼ C i ðt t0 ÞA xðt0 Þ ¼ i ðt t0 ÞCA xðt0 Þ i¼0
i¼0
The above relation can be written in compact matrix form as follows: yðtÞ ¼ ERxðt0 Þ
ð5:6-13Þ
where 2 . . . . E ¼ ½0 I .. 1 I .. 2 I .. .. n1 I
and
6 6 R¼6 6 4
C CA CA2 .. .
3 7 7 7 7 5
CAn1 From linear algebra it is well known that for system (5.6-13) to have a solution for xðt0 Þ, the rank of matrix R, or equivalently the rank of its transpose matrix . . . . RT ¼ ½CT .. AT CT .. ðAT Þ2 CT .. .. ðAT Þn1 CT must be equal to n. Remark 5.6.3 Theorems 5.6.2 and 5.6.5 are dual, in the sense that the role of the matrices B and A in the controllability matrix S play the matrices CT and AT in the observability matrix RT . The duality of S and RT also appears in transforming a system to its phase canonical form. In Subsec. 5.4.2 we presented a method of transforming system ðA; B; C; DÞn to its phase canonical form ðA* ; B* ; C* ; D* Þn based on the controllability matrix S, where the matrices A* and B* have special forms. In an analogous way, system ðA; B; C; DÞn can be transformed to its phase canonical form ðAþ ; Bþ ; Cþ ; Dþ Þn based on the observability matrix R, where the matrices Aþ and Cþ have special forms. The forms of the matrices A* and Aþ , and of the matrices B* and Cþ , are dual. In order to distinguish these two cases, we say that system ðA* ; B* ; C* ; D* Þn is in its input phase canonical form, whereas system ðAþ ; Bþ ; Cþ ; Dþ Þn is in its output phase canonical form. The example that follows refers to the determination of the output phase canonical form. Example 5.6.4 Determine if the state vector of a system with matrices
222
Chapter 5
2
1 0 A ¼ 4 0 2 0 0
3 0 0 5; 3
2
3 0 0 B ¼ 4 1 0 5; 0 1
and
C ¼ ½1 1
1
is observable. Furthermore, determine the output phase canonical form of the system. Solution Construct the matrix RT :
2
61 . . RT ¼ ½CT .. AT CT .. ðAT Þ2 CT ¼ 6 41 1
.. . .. . .. .
1 2 3
.. . .. . .. .
3 17 7 45 9
Since rankR ¼ 3, it follows that the state vector of the given system is observable. To determine its output phase canonical form, we have 3 3 2 2 2 3 q3 6 6 2 1 2 1 16 7 7 7 16 6 P ¼ 4 q3 AT 5 ¼ 4 1 4 3 5; ðRT Þ1 ¼ 4 5 8 3 5; 2 2 1 2 1 1 8 9 q3 ðAT Þ2 2 3 6 5 1 6 7 T ¼ P1 ¼ 4 3 4 1 5 2
3
1
where q3 is the last row of ðRT Þ1 . The matrices of the given system in output phase canonical form are 2 3 0 1 0 3 4 1 0 1 5; ðBþ ÞT ¼ BT T ¼ ðAþ ÞT ¼ T1 AT T ¼ 4 0 2 3 1 6 11 6 2 3 0 þ T 1 T ðC Þ ¼ T C ¼ 4 0 5 1 5.6.4
The Invariance of Controllability and Observability
The properties of controllability and observability are invariant under state vector similarity transformation. Indeed, the matrices S* , Q* , and R* of the transformed system ðA* ; B* ; C* ; D* Þn are related to the matrices S, Q, and R of the original system ðA; B; C; DÞn as follows: . . . . S* ¼ ½B* .. A* B* .. ðA* Þ2 B* .. .. ðA* Þn1 B* . . . . ¼ ½T1 B .. T1 ATT1 B .. ðT1 ATÞ2 T1 B .. .. ðT1 ATÞn1 T1 B . . . . ¼ ½T1 B .. T1 AB .. T1 A2 B .. .. T1 An1 B . . . . ¼ T1 ½B .. AB .. A2 B .. .. An1 B ¼ T1 S
ð5:6-14Þ
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223
Furthermore . . . . Q* ¼ ½D* .. C* B* .. C* A* B* .. .. C* ðA* Þn1 B* . . . . ¼ ½D .. CTT1 B .. CTT1 ATT1 B .. .. CTðT1 ATÞn1 T1 B . . . . ¼ ½D .. CB .. CAB .. .. CAn1 B ¼ Q
ð5:6-15Þ
Finally, . . . . R* T ¼ ½C* T .. A* T C* T .. ðA* T Þ2 C* T .. .. ðA* T Þn1 C* T . . . . ¼ ½TT CT .. TT AT ðT1 ÞT TT CT .. ½TT AT ðT1 ÞT 2 TT CT .. .. ½TT AT ðT1 ÞT n1 TT CT . . ¼ ½TT CT .. TT AT CT .. TT ðAT Þ2 CT . . . ¼ TT ½CT .. AT CT .. ðAT Þ2 CT ..
.. . . .. TT ðAT Þn1 CT .. T n1 T . ðA Þ C ¼ TT RT
ð5:6-16Þ
From Eqs (5.6-14)–(5.6-16) it follows that, if the original system is controllable and/ or observable, then the transformed system is also controllable and/or observable. 5.6.5
Relation Among Controllability, Observability, and Transfer Function Matrix
It is clear that the transfer function matrix HðsÞ is an input–output description of a system. That is, it relates the input vector UðsÞ to the output vector YðsÞ of the system without involving the state vector XðsÞ. At this point, we raise the following question: Is the transfer function matrix HðsÞ affected and how by the properties of controllability and observability of the system? The answer to this question is of great importance and constitutes one of the basic reasons for preferring the state equations over transfer function matrices for describing control systems. In the sequel, we will try to give the answer to this question. We introduce the following definition. Definition 5.6.4 Consider the sequences . . . Sj ¼ ½B .. AB .. .. Aj1 B
ð5:6-17Þ
. . . RTj ¼ ½CT .. AT CT .. .. ðAT Þ j1 CT
ð5:6-18Þ
Let and be the smallest positive integer numbers such that rankS ¼ rankSþ1 and rankR ¼ rankR þ1 (therefore rankSi ¼ rankS , 8i > and rankRi ¼ rankR , 8i > ). Then, the index is called the controllability index and the index is called the observability index. It has been proven that there is a strong relationship among the three system characteristics: (a) controllability and observability; (b) the matrices S and R ; and (c) the minimum state-space realization (see Subsec. 3.8.5). This strong relationship is stated in the following theorem.
224
Chapter 5
Theorem 5.6.6 For system (5.1-1), the following three propositions are equivalent: (a)
The system is observable and controllable—namely, the rank of both matrices S and R is n. (b) RankR S ¼ n. (c) The dimension n of the state-space realization is minimum.
The following theorem relates the classical and the modern control theory, since it relates the classical input–output description HðsÞ to the modern description of a system in state space. This relation has come to light from the concepts of controllability and observability. Theorem 5.6.7 If the transfer function matrix of a system involves pole-zero cancellations, then the system is either uncontrollable or unobservable or both. If the transfer function matrix does not involve any pole-zero cancellation, then the system is both controllable and observable. Proof Consider an SISO system which is already in diagonal form, i.e., consider the following diagonal system x ¼ ,x ¼ bu
ð5:6-19aÞ
y¼c x
ð5:6-19bÞ
T
For this system we have x_ i ¼ i xi þ bi u;
i ¼ 1; 2; . . . ; n
or Xi ðsÞ ¼
bi UðsÞ; s i
i ¼ 1; 2; . . . ; n
ð5:6-20Þ
where bi is the ith element of b. Therefore YðsÞ ¼ cT XðsÞ ¼
n X i¼1
ci Xi ðsÞ ¼
n X c i bi UðsÞ s i i¼1
ð5:6-21Þ
where ci is the ith element of the vector c. The transfer function HðsÞ of the system has the general form HðsÞ ¼ K
ðs z1 Þðs z2 Þ ðs zm Þ ; ðs 1 Þðs 2 Þ ðs n Þ
m
If we expand HðsÞ in partial functions, we have HðsÞ ¼
n X hi s i i¼1
ð5:6-22Þ
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225
Comparing Eqs (5.6-21) and (5.6-22), it follows that hi ¼ c i bi
ð5:6-23Þ
Consequently, if HðsÞ involves cancellation of the pole i , then there must be hi ¼ 0. From Eq. (5.6-23) it follows that for hi ¼ 0 to hold, then there must be either bi ¼ 0 or ci ¼ 0, or even bi ¼ ci ¼ 0. Clearly, if bi ¼ 0, then the system is uncontrollable (because one of the rows of b is zero). If ci ¼ 0, then the system is unobservable (because one of the columns of cT is zero). Finally, if bi ¼ ci ¼ 0, then the system is both uncontrollable and unobservable. If hi 6¼ 0, 8i, then all ci ’s and bi ’s are different than zero and hence system (5.619) is both controllable and observable. The following theorem is of great practical importance. Theorem 5.6.8 The transfer function matrix involves only the controllable and observable part of a system. The practical importance of the previous theorems, and particularly of Theorem 5.6.8, is demonstrated by the following examples. Example 5.6.5 Consider the network of Figure 5.7. Determine the transfer function and study the case of pole-zero cancellations. Solution The transfer function of the network is HðsÞ ¼
YðsÞ Z2 ðsÞ ¼ UðsÞ Z1 ðsÞ þ Z2 ðsÞ
where
Figure 5.7
RC network.
226
Chapter 5
1 R1 C1 s ; ¼ Z1 ðsÞ ¼ 1 R1 C1 s þ 1 R1 þ C1 s
1 R2 C2 s Z2 ðsÞ ¼ ¼ 1 R2 C2 s þ 1 R2 þ C2 s
R1
R2
Furthermore, R2 Rc C2 s þ 1
HðsÞ ¼
R1 R2 þ R1 C1 s þ 1 R2 C2 s þ 1 C1 sþ ¼ C1 þ C2 s þ
¼
R2 ðR1 C1 s þ 1Þ R1 ðR2 C2 s þ 1Þ þ R2 ðR1 C1 s þ 1Þ
where ¼
1 ; R1 C1
¼
R1 þ R2 R1 R2 ðC1 þ C2 Þ
If we choose R1 ; R2 ; C1 , and C2 such that R1 C1 ¼ R2 C2 , then ¼ and HðsÞ ¼
C1 R2 ¼ ¼ constant C1 þ C2 R1 þ R2
The above results indicate that, in general, HðsÞ is a function of s. However, in the special case where R1 C1 ¼ R2 C2 the transfer function HðsÞ reduces to a constant. In this case, HðsÞ gives misleading information about the network, because one may arrive at the conclusion that the RC network is a pure resistive network, which it is not. Furthermore, when R1 C1 ¼ R2 C2 , the network is neither controllable (the voltages of the capacitors C1 and C2 cannot be controlled) nor observable (the initial voltages of the capacitors C1 and C2 cannot be estimated from input and output data). Example 5.6.6 Consider the SISO system x ¼ Ax þ bu, y ¼ cT x, where 1 0 0 1 ; and c ¼ ; b¼ A¼ 1 1 2 1 Determine if the system is controllable and observable and find its transfer function. Solution Since the system is already in its phase canonical form, it immediately follows that it is controllable. To determine if the system is observable, construct the matrix " # .. . 2 RT ¼ ½c .. AT c ¼ 1 .. 1 .. 2 Since jRj ¼ 0, it follows that rankR < 2, and hence the system is not observable. The transfer function of the system is HðsÞ ¼ cT ðsI AÞ1 b ¼
s1 1 ¼ ðs 1Þðs þ 2Þ s þ 2
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227
Clearly, since the system is unobservable, it was expected that at least one pole-zero cancellation will take place. The cancellation of the factor ðs 1Þ results in ‘‘concealing’’ the eigenvalue 1. This can have extremely undesirable results, particularly in cases like the present example, where the cancelled out pole is in the right-half complex plane. In this case, if the system were excited, the states of the system would increase with time, and could even break down or burn out the system. Indeed, if, for simplicity, we let uðtÞ ¼ ðtÞ, then 1 ðs 1Þðs þ 2Þ s X2 ðsÞ ¼ ðs 1Þðs þ 2Þ
X1 ðsÞ ¼
and thus and thus
1 x1 ðtÞ ¼ ðet e2t Þ 3 1 x2 ðtÞ ¼ ðet þ 2e2t Þ 3
Hence, due to the term et , both x1 ðtÞ and x2 ðtÞ increase with time. Now, consider determining the output on the bases of the transfer function. We have YðsÞ ¼ HðsÞ UðsÞ ¼
1 ðs þ 2Þ
and thus
yðtÞ ¼ e2t
Clearly, the output gives very misleading results as to the behavior of the system: in reality, the system may break down or burn out and the output leads us to believe that there is ‘‘no problem’’ and that the system’s behavior is described by the decaying function yðtÞ ¼ e2t . This leads to the conclusion that we should not ‘‘trust’’ HðsÞ unless the following theorem is satisfied. Theorem 5.6.9 When a system is controllable and observable, then no pole-zero cancellations take place and its transfer function matrix constitutes a complete description of the system.
5.7
KALMAN DECOMPOSITION
Kalman showed that it is possible to introduce certain coordinates, using a suitable transformation matrix T, such that a system can be decomposed as follows: 3 2 3 2 B1 A11 A12 0 0 7 6 0 7 0 0 0 A xðtÞ ¼ 6 22 7 6 6 7 ð5:7-1aÞ 4 A31 A32 A33 A34 5xðtÞ þ 4 B3 5uðtÞ 0 A42 0 A44 0 yðtÞ ¼ ½ C1
C2
0
0
xðtÞ
ð5:7-1bÞ
where Aij , Bi , and Ci are block matrices of suitable dimensions. The state-space vector xðtÞ is accordingly decomposed into four subvectors, each one of which corresponds to one of the following four cases: . . . .
States States States States
that that that that
are are are are
both controllable and observable uncontrollable but are observable controllable but unobservable both uncontrollable and unobservable.
228
Chapter 5
Figure 5.8
Block diagram of the Kalman decomposition.
The following theorem, which is related to the above results, is called the Kalman decomposition theorem. Theorem 5.7.1 A system can be decomposed into four subsystems with the following properties: . . . .
Subsystem SOC : the observable and controllable subsystem ðA11 ; B1 ; C1 Þ. Subsystem SOC : the observable but uncontrollable subsystem ðA22 ; 0; C2 Þ. Subsystem SO C : the unobservable but controllable subsystem ðA33 ; B3 ; 0Þ. Subsystem SO C : the unobservable and uncontrollable subsystem ðA44 ; 0; 0Þ.
The transfer function matrix of system (5.7-1) is unique and can be determined from the subsystem which is both controllable and observable. Indeed, straightforward calculations show that the transfer function matrix of system (5.7-1) is given by HðsÞ ¼ C1 ðsI A11 Þ1 B1
ð5:7-2Þ
Relation (5.7-2) contains only the controllable and the observable part of the system. Figure 5.8 shows the block diagram of the Kalman decomposition, involving all four subsystems and the way in which they are linked to each other. Also, it shows that the input is related to the output only through the subsystem SOC . 5.8
PROBLEMS
1. Consider the frictionless horizontal movement of the mass m shown in Figure 5.9a. A force f ðtÞ is applied to the mass. The displacement and the velocity of the mass are yðtÞ and vðtÞ, respectively. The input, state, and output variables are defined in Figure 5.9b. Determine (a) the state equations of the system, (b) the transition matrix, (c) the solution of the homogeneous equation, and (d) the general solution, given that f ðtÞ ¼ f0 uðtÞ, and (e) the output of the system.
State-Space Analysis
229
Figure 5.9 2. For the RLC network shown in Figure 5.10a, determine (a) the state equations, using the definitions of input, state, and output variables shown in Figure 5.10b, (b) the transition matrix, (c) the solution of the homogeneous equation, (d) the general solution, and (e) the output vector of the system. 3. Consider the mechanical system shown in Figure 5.11a, where the two inputs are the forces f1 ðtÞ and f2 ðtÞ applied to the masses m1 and m2 , respectively. The two outputs are the displacements y1 ðtÞ and y2 ðtÞ of the masses. Using the definitions shown in Figure 5.11b, determine (a) the state equations of the system and (b) the transition matrix and the output vector of the system, when m1 ¼ m2 ¼ 1 kg, K1 ¼ 1, K2 ¼ 2, B1 ¼ 1, f1 ðtÞ ¼ f2 ðtÞ ¼ 1, y1 ð0Þ ¼ 1, y_ 1 ð0Þ ¼ y2 ð0Þ ¼ y_2 ð0Þ ¼ 0: 4. Consider the liquid-level control system shown in Figure 5.12a. Using the definitions of Figure 5.12b, determine (a) the state equations of the system and (b) the transition matrix and the output vector when the horizontal area of both tanks is A ¼ 1 m2 and the flow resistance at the output of both tanks is R ¼ 1. 5. A system of two carts connected by a damper is shown in Figure 5.13a. The input, state, and output variables are defined in Figure 5.13b, where f is the force applied, v1 and v2 are the velocities of the two carts, and the difference v2 v1 is the system output. (a) Determine the state equations and (b) transform the system into phase canonical form. 6. A control system is described by the following state equations: 0 1 x1 0 x_ 1 ¼ þ u 2 3 x2 2 x_ 2 y ¼ ½1
0
x1 x2
230
Figure 5.10
Figure 5.11
Chapter 5
State-Space Analysis
Figure 5.12
Figure 5.13
231
232
Chapter 5
(a) Transform the system into diagonal form and (b) determine the general solution for the transformed system, given that x1 ð0Þ ¼ x2 ð0Þ ¼ 0 and uðtÞ ¼ et , t 0. 7. Diagonalize the following matrices
2
1 ; 3
0 2
0 40 0
1 0 1
2
3 0 1 5; 0
0 60 6 40 0
1 0 0 2
3 0 07 7 15 2
0 1 0 1
8. Derive the block diagrams and the signal-flow graphs for the following systems 2
3
2
1 6s ðaÞ HðsÞ ¼ 6 4 0
2 ðcÞ
0
7 7 1 5 sþ1
1
6 x ¼ 6 40 6
0
0 11
ðbÞ
1 6 0 s 6 6 1 6 HðsÞ ¼ 6 0 6s þ 1 6 4 0 1 2
2 3 0 6 7 7 6 7 17 5x þ 4 0 5u; 1 6 0
3
1
3 1 s þ 27 7 7 7 0 7 7 7 1 5 sþ3
3
7 6 7 xð0Þ ¼ 6 4 1 5 0
9. A simplified picture of a submarine is shown in Figure 5.14a. In Figure 5.14b the input, state, and output variables of the system are defined. The state equations describing the system are the following: 2
x_ 1
3
2
0
6 7 6 6 x_ 2 7 ¼ 6 1 4 5 4 0 x_ 3
1 3
0
32
x1
3
2
0
3
76 7 6 7 7 6 7 6 27 54 x2 5 þ 4 1 5u 1 1 x3
2
y ¼ ½1
3 x1 0 0 4 x2 5 x3
Examine the controllability and observability of the system. 10. Consider the electrical network given in Figure 5.15a. Find its state equations using the definitions of input, state, and output variables shown in Figure 5.15b. Investigate the observability of the system in relation to the free values of the network elements. 11. Investigate the controllability and observability of the following systems. Furthermore, transform them into their phase canonical input and output forms:
State-Space Analysis
Figure 5.14
Figure 5.15
233
234
Chapter 5
2
3 2 3 1 0 1 1 ðaÞ A ¼ 4 2 2 0 5; b ¼ 4 1 5; cT ¼ ½ 1 0 1 1 2 1 0 3 2 3 2 0 1 2 1 0 617 6 0 1 4 1 7 1 1 1 0 7 7 6 6 ; b ¼ 4 5; C¼ ðbÞ A ¼ 4 0 1 0 0 1 0 0 1 05 1 1 0 0 0 2 3 2 3 1 0 0 1 1 ðcÞ A ¼ 4 0 1 1 5; B ¼ 4 0 1 5; cT ¼ ½ 1 1 1 1 0 0 1 0 12. Consider the network of Figure 5.16a, where R1 ¼ R2 ¼ 1 , C1 ¼ C2 ¼ 1 F, L ¼ 2 H, vc1 ð0Þ ¼ 1 V, vc2 ð0Þ ¼ 1 V, iL ð0Þ ¼ 0 A. The input, state, and output variables are defined in Figure 5.16b. Determine (a) the state equations, (b) the state transition matrix, (c) the output of the network, (d) the state and output controllability, (e) the state observability, and (f) a phase canonical form description.
Figure 5.16
State-Space Analysis
235
13. A system is described by the matrix equations x ¼ Ax þ Bu; y ¼ Cx þ Du; xð0Þ ¼ 0 where
2
0 60 A¼6 40 0
0 D¼ 0
1 0 0 0 0 1
0 1 0 0
3 0 07 7; 15 1
2
0 60 B¼6 41 0
3 0 07 7; 05 1
1 C¼ 0
0 0
0 0
0 ; 1
Determine (a) the matrix ðsI AÞ1 , using the Leverrier’s algorithm, (b) the state transition matrix, (c) the transfer function matrix, (d) the output vector yðtÞ, and (e) the state and output controllability and the state observability. 14. Solve Problem 13 for the system with matrices 2 3 2 3 0 1 0 0 0 1 1 1 A ¼ 4 2 3 0 5; B ¼ 4 1 0 5; C¼ ; 0 0 1 1 1 1 0 1
1 D¼ 0
0 0
and initial state vector xT ð0Þ ¼ ½1; 1; 0. BIBLIOGRAPHY Books 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.
PJ Antsaklis, AN Michel. Linear Systems. New York: McGraw-Hill, 1997. RW Brockett. Finite Dimensional Linear Systems. New York: John Wiley, 1970. WL Brogan. Modern Control Theory. 2nd ed. Englewood Cliffs, New Jersey: Prentice Hall, 1985. FM Callier, CA Desoer. Multivariable Feedback Systems. New York: Springer-Verlag, 1982. CT Chen. Linear System Theory and Design. New York: Holt, Rinehart and Winston, 1984. RA DeCarlo. Linear Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1989. PM DeRusso, RJ Roy, CM Close. State Variables for Engineers. New York: John Wiley, 1965. JC Doyle. Feedback Control Theory. New York: Macmillan, 1992. B Friedland. Control System Design. An Introduction to State-Space Methods. New York: McGraw-Hill, 1987. RA Gabel, RA Roberts. Signals and Linear Systems. 3rd ed. New York: John Wiley & Sons, 1987. T Kailath. Linear Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1980. BC Kuo. Automatic Control Systems. London: Prentice Hall, 1995. JM Maciejowski. Multivariable Feedback Design. Reading, MA: Addison-Wesley, 1989. NS Nise. Control Systems Engineering. New York: Benjamin and Cummings, 1995. K Ogata. Modern Control Systems. London: Prentice Hall, 1997.
236
Chapter 5
16. CE Rohrs, JL Melsa, D Schultz. Linear Control Systems. New York: McGraw-Hill, 1993. 17. HH Rosenbrock. State Space and Multivariable Theory. London: Nelson, 1970. 18. HH Rosenbrock. Computer Aided Control System Design. New York: Academic Press, 1974. 19. WJ Rugh. Linear System Theory. 2nd ed. Englewood Cliffs, New Jersey: Prentice Hall, 1996. 20. RJ Schwarz, B Friedland. Linear Systems. New York: McGraw-Hill, 1965. 21. LK Timothy, BE Bona. State Space Analysis: An Introduction. New York: McGrawHill, 1968. 22. DM Wiberg. Theory and Problems of State-Space and Linear Systems. Schaums Outline Series. New York: McGraw-Hill, 1971. 23. LA Zadeh, CA Desoer. Linear Systems Theory – The State Space Approach. New York: McGraw-Hill, 1963.
Articles 24. RE Kalman. A new approach to linear filtering and prediction problems. Trans ASME (J Basic Engineering) 82D:35–45, 1960. 25. RE Kalman. On the general theory of control systems. Proceedings First International Congress, IFAC, Moscow, USSR, 1960, pp 481–492. 26. RE Kalman. Contributions to the theory of optimal control. Proceedings 1959 Mexico City Conference on Differential Equations, Mexico City, 1960, pp 102–119. 27. RE Kalman. Canonical structure of linear dynamical systems. Proceedings National Academy of Science 48:596–600, 1962. 28. RE Kalman. Mathematical description of linear dynamic systems. SIAM J Control 1:152–192, 1963. 29. RE Kalman, RS Bucy. New results in linear filtering and prediction theory. Trans ASME (J Basic Engineering) 83D:95–108, 1961. 30. RE Kalman, YC Ho, KS Narenda. Controllability of linear dynamic systems. Contributions to Differential Equations 1:189–213, 1963. 31. BC Moore. Principal component analysis in linear systems: controllability, observability and model reduction. IEEE Trans Automatic Control AC-26:17–32, 1981.
6 Stability
6.1
INTRODUCTION
Systems have several properties—such as controllability, observability, stability, and invertibility—that play a very decisive role in their behavior. From these characteristics, stability plays the most important role. The most basic practical control problem is the design of a closed-loop system such that its output follows its input as closely as possible (see Chap. 4). In the present chapter we will show that unstable systems cannot guarantee such behavior and therefore are not useful in practice. Another serious disadvantage of unstable systems is that the amplitude of at least one of their state and/or output variables tends to infinity as time increases, even though the input of the system is bounded. This usually results in driving the system to saturation and in certain cases the consequences may be even more undesirable: the system may suffer serious damage, such as burn out, break down, explosion, etc. For these and other reasons, in designing an automatic control system, our primary goal is to guarantee stability. As soon as stability is guaranteed, then one seeks to satisfy other design requirements, such as speed of response, settling time, bandwidth, and steady-state error. The concept of stability has been studied in depth, and various criteria for testing the stability of a system have been proposed. Among the most celebrated stability criteria are those of Routh, Hurwitz, Nyquist, Bode, Nichols, and Lyapunov. From these criteria, the first five are in the frequency domain, whereas the last one is in the time domain. These criteria are presented in this chapter, except the criteria of Nyquist, Bode, and Nichols, which are presented in Chap. 8. The very popular root locus technique proposed by Evans, which also facilitates the study of stability, is presented in Chap. 7.
6.2
STABILITY DEFINITIONS
In this section, the stability of linear, time-invariant systems is studied in connection with each of the three well-established mathematical models of a system—namely, the state equations, the transfer function matrix, and the impulse response matrix. 237
238
Chapter 6
1 Stability of Systems Described in State Space Consider a linear, time-invariant system described in state space as follows x ¼ Ax þ Bu; xð0Þ ¼ x0 6¼ 0 ð6:2-1aÞ y ¼ Cx þ Du
ð6:2-1bÞ
The stability considered here refers to the zero-input case, i.e., to the case where the sysem (6.2-1) has zero input ðuðtÞ ¼ 0Þ. This is the case of natural (or free) response of the system, also known as the homogeneous solution (see relation (4.2-3)). The stability defined for uðtÞ ¼ 0 is called zero-input stability. a Asymptotic Stability System (6.2-1) is asymptotically stable if, for uðtÞ ¼ 0 and for every finite initial state xð0Þ 6¼ 0, the following condition is satisfied lim kxðtÞk ¼ 0
t!1
ð6:2-2Þ
where k:k stands for the Euclidean norm of the vector xðtÞ, i.e., kxðtÞk ¼ ðx21 þ x22 þ þ x2n Þ1=2 A simplified picture of the above definition is given in Figure 6.1, where, for simplicity, we consider the case where the state vector has only two variables. In the figure it is shown that the state vector xðtÞ of the system (6.2-1) with uðtÞ ¼ 0 and initial conditions xð0Þ moves towards the origin when condition (6.2-2) is satisfied.
Figure 6.1
A simplified picture of stability of systems described in state space.
Stability
239
b Stability in the Circle M System (6.2-1) is stable in the circle M if, for uðtÞ ¼ 0 and for every finite initial state xð0Þ 6¼ 0, the following condition is satisfied: kxðtÞk < M;
t0
ð6:2-3Þ
In this case, xðtÞ remains within a circle of finite radius M, as shown in Figure 6.1. c Instability If xðtÞ does not satisfy condition (6.2-3), the system is said to be unstable. In this case, the vector xðtÞ, for uðtÞ ¼ 0 and initial state xð0Þ 6¼ 0, moves towards infinity as t ! 1, as shown in Figure 6.1. 2
Stability of Systems Described by Their Transfer Function Matrix
The previous definition of stability refers to systems described in state space and relates their stability with the Euclidean norm of the state vector. For systems described by their transfer function matrix HðsÞ, the asymptotic stability condition follows directly from the asymptotic stability definition (6.2-2). Here, the stability is exclusively related to the poles of HðsÞ. This marks out the great importance that the poles of a system have in relation to its stability. Consider a linear, time-invariant system described in the s domain by its transfer function matrix HðsÞ ¼ C½sI A1 B þ D The characteristic polynomial of the system is n Y pðsÞ ¼ jsI Aj ¼ ðs i Þ
ð6:2-4Þ
i¼1
where 1 ; 2 ; . . . ; n are the eigenvalues of A, or equivalently, the poles of HðsÞ. a Asymptotic Stability System (6.2-4) is asymptotically stable if all the poles 1 ; 2 ; . . . ; n of HðsÞ lie in the left-half complex plane, i.e., if the following condition holds: i ¼ 1; 2; . . . ; n ð6:2-5Þ Re i < 0; In this case, the natural response of the system goes to zero as time goes to infinity. b Marginal Stability System (6.2-4) is marginally stable if there exist poles on the imaginary axis of multiplicity one. The rest of the poles are in the left-half complex plane. In this case the natural response of the system neither grows nor decays. It remains constant or it oscillates with constant amplitude. It is remarked that stability in the circle M and marginal stability are essentially equivalent. Remark 6.2.1 In marginal stability, we tacitly assume that none of the poles of the system excitation uðtÞ coincides with any of the poles of HðsÞ on the imaginary axis. In the opposite case, the system is unstable. For example, let UðsÞ ¼ 1=s and HðsÞ ¼ 1=sðs þ 1Þ. Then YðsÞ ¼ HðsÞUðsÞ ¼ 1=s2 ðs þ 1Þ and, hence, the system is unstable. Similarly, for conjugate poles on the imaginary axis. Let UðsÞ ¼ 1=ðs2 þ 1Þ (i.e., let uðtÞ ¼ sin t) and
240
Chapter 6
HðsÞ ¼ 1=ðs þ 4Þðs2 þ 1Þ; then YðsÞ ¼ HðsÞUðsÞ ¼ 1=ðs þ 4Þðs2 þ 1Þ2 , and hence the system is unstable. Remark 6.2.2 In certain special cases the system is intended to be marginally stable: as, for example, in the case of an integrator ðHðsÞ ¼ K=sÞ or an oscillator ðHðsÞ ¼ Ks=ðs2 þ !2n ÞÞ. In these special cases, we make an exception to the rule and we usually say that the system is stable (even though it is marginally stable). c Instability System (6.2-4) is unstable if it has at least one pole in the right-half complex plane or it has poles on the imaginary axis of multiplicity greater than 1. In this case, as time approaches infinity, the natural response of the system approaches infinity. 3 Stability of Systems Described by their Impulse Response Matrix For systems described by their impulse response matrix HðtÞ, the asymptotic stability condition follows directly from the asymptotic stability definition (6.2-2). Here, the stability is related to the absolute value of each element or to the integral of the absolute value of each element of HðtÞ. Consider a linear, time-invariant system described in the time domain by its impulse response matrix HðtÞ ¼ L1 fCðsI AÞ1 B þ Dg ¼ CeAt B þ DðtÞ;
t0
ð6:2-6Þ
a Asymptotic Stability System (6.2-6) is asymptotically stable if the following condition is satisfied: ð1 jhij ðtÞj dt < A; 8i; j ð6:2-7Þ 0
where A is a finite positive constant and hij ðtÞ is the ði; jÞth element of the impulse response matrix HðtÞ. b Marginal Stability System (6.2-6) is marginally stable if the following conditon holds jhij ðtÞj < B;
8i; j
ð6:2-8Þ
where B is a finite positive number. An analogous remark to Remark 6.2.1 holds for the present case. c Instability System (6.2-6) is unstable even if one element hij ðtÞ of HðtÞ does not satisfy Eq. (6.2-8). At this point, we briefly introduce another definition for stability, called the bounded-input–bounded-output (BIBO) stability. A system is BIBO stable if, for any bounded input, its output is also bounded. This definition is more general than all previous definitions, because it refers to systems that may not be linear and time invariant, while it gives a simple picture of the concept of stability. For single-input–single-output (SISO) systems, BIBO stability may be interpreted as follows. Consider the bounded input uðtÞ, i.e., assume that juðtÞj C1 , for t 2 ð0; 1Þ, where C1 is a finite constant. Also assume that the response of the system to this input is yðtÞ which is also bounded, i.e., assume that
Stability
241
jyðtÞj C2 , for t 2 ð0; 1Þ, where C2 is a finite constant. If for all possible bounded inputs the corresponding outputs of the system are also bounded, then the system is said to be BIBO stable. Finally, it is mentioned that asymptotic stability is the strongest, since it is more stringent than marginal stability and BIBO stability. Example 6.2.1 Investigate the stability of a system described in state space in the form (6.2-1), where 0 1 1 0 1 ; b¼ ; c¼ ; D ¼ 0; xð0Þ ¼ A¼ 0 1 2 3 1 Solution The state vector, for uðtÞ ¼ 0, will be
9 82 3 sþ3 1 > " #> > = <6 ðs þ 1Þðs þ 2Þ ðs þ 1Þðs þ 2Þ 7 x1 ð0Þ > 7 xðtÞ ¼ L1 fðsI AÞ1 xð0Þg ¼ L1 6 5 x ð0Þ > 4 > 2 s > > 2 : ; ðs þ 1Þðs þ 2Þ ðs þ 1Þðs þ 2Þ " #" # " t # 1 e 2et e2t et e2t ¼ ¼ t 2t t 2t 1 et 2e þ 2e e þ 2e
The norm of the vector xðtÞ is kxðtÞk ¼ ½x21 ðtÞ þ x22 ðtÞ1=2 ¼ ½e2t þ e2t 1=2 ¼ ½2e2t 1=2 ¼
pffiffiffi t 2e
It is clear that, on the basispof ffiffiffi the definition of asymptotic stability, condition (6.2-2) is satisfied, since kxðtÞk < 2 for t > 0 and lim kxðtÞk ! 0. Therefore, the system is t!1 asymptotically stable. The characteristic polynomial of the system is pðsÞ ¼ jsI Aj ¼ ðs þ 1Þðs þ 2Þ The two eigenvalues of the matrix A are 1 and 2, and they both lie in the left-half complex plane. Therefore, the system, on the basis of condition (6.2-5), is asymptotically stable. The impulse response of the system is hðtÞ ¼ cT eAt b ¼ cT Me,t M1 b where M is a transformation matrix which transforms the matrix A in its diagonal form ,. According to the results of Subsec. 5.4.4, the matrix M has the form 1 1 2 1 1 1 M¼ ¼ ; M1 ¼ 1 2 1 1 1 2 Therefore, hðtÞ ¼ c Me,t M1 b ¼ ½1; 0 T
¼e
t
e
2t
1 1
1 2
et 0
0
e2t
2 1 1 1
0 1
242
Chapter 6
If we apply condition (6.2-7), we have ð1 ð1 ð1 ð1 t 2t t jhðtÞj dt ¼ je e j dt e dt þ e2t dt ¼ 1 þ 0:5 ¼ 1:5 < 1 0
0
0
0
Hence, the system, on the basis of condition (6.2-7), is asymptotically stable. Assume that the system is excited by a bounded input juðtÞj < C < 1. Then, the absolute value of the output of the system will be ðt ð t ðt jyðtÞj ¼ hðÞuðt Þd jhðÞjjuðt Þj d ¼ C jhðÞj d 1:5C < 1 0
0
0
Hence, on the basis of definition of the BIBO stability, it is concluded that the system is BIBO stable. Example 6.2.2 Investigate the stability of a system described in state-space form (6.2-1), where 1 0 0 0 1 ; D ¼ 0; xð0Þ ¼ ; c¼ ; b¼ A¼ 1 1 1 1 0 Solution The state vector, for uðtÞ ¼ 0, will be
82 39 s 1 > > # > >" <6 2 2 7= x1 ð0Þ 1 1 1 6 s 1 s 1 7 xðtÞ ¼ L fðsI AÞ xð0Þg ¼ L 4 1 > s 5> > > : ; x2 ð0Þ 2 2 s 1 s 1 "1 t #" # " t # t t 1 t 1 e 2 ðe þ e Þ 2 ðe e Þ ¼ ¼ t t t t 1 1 1 et 2 ðe e Þ 2 ðe þ e Þ
The norm of the state vector is kxðtÞk ¼ ½x21 ðtÞ þ x22 ðtÞ1=2 ¼ ½2e2t 1=2 ¼
pffiffiffi t 2e
It is clear that the system is unstable because, as t ! 1, xðtÞ tends to infinity. The characteristic polynomial of the system is pðsÞ ¼ jsI Aj ¼ ðs 1Þðs þ 1Þ The two eigenvalues of the matrix A are 1 and 1. From these two eigenvalues, one lies in the right-half complex plane and therefore the system is unstable. The impulse response of the system is hðtÞ ¼ L1 fcT ðsI AÞ1 bg ¼ 12 ðet þ et Þ If we apply condition (6.2-7) we have ð1 ð ð ð 1 1 t 1 1 t 1 1 t jhðtÞj dt ¼ je þ et j dt e dt þ e dt ¼ 1 2 0 2 0 2 0 0 Hence the system, is unstable. Assume that the system is excited by a bounded input juðtÞj < C < 1. Then, the absolute value of the output of the system will be
Stability
243
ð t ðt ðt jyðtÞj ¼ hðÞuðt Þ d jhðÞjjuðt Þj d ¼ C jhðÞj d 0
0
0
Therefore ðt lim jyðtÞj ¼ C lim
t!1
t!1 0
jhðÞj d ¼ 1
Hence the system is not BIBO stable. A summary of the main points of the present section is given in Figure 6.2.
6.3
STABILITY CRITERIA
Clearly, each of the definitions of Sec. 6.2 may be applied to study the stability of a system. Their application, however, appears to have many difficulties. For example, the definition based on the state-space description requires the determination of the state vector xðtÞ. This computation is usually quite difficult. The definition based on the transfer function matrix HðsÞ requires the computation of the roots of the characteristic polynomial jsI Aj. This computation becomes more complex as the degree of the characteristic polynomial becomes greater. The definition based on the impulse response matrix HðtÞ requires the determination of the impulse response matrix HðtÞ. This appears to have about the same difficulties as the determination of the transition matrix rðtÞ. The BIBO definition appears to be simple and practical to apply, but because of its very nature, it is almost impossible to use. This is because in order to study the stability of a system on the basis of the BIBO stability definition, one must examine all possible bounded inputs, which requires an infinitely long period of time. From all different definitions mentioned above, the definition based on the transfer function description appears to offer, from the computational point of view, the simplest approach. But even in this case, particularly when the degree of the characteristic polynomial is very high, the determination of the poles could involve numerical difficulties which might make it difficult, if not impossible, to apply. From the above, one may conclude that in practice it is very difficult to apply the definitions of stability presented in Sec. 6.2 directly in order to study the stability of a system. To circumvent this difficulty, various stability criteria have been developed. These criteria give pertinent information regarding the stability of a system without directly applying the definitions for stability and without requiring complicated numerical procedures. The most popular criteria are the following: 1.
The algebraic criteria: these criteria assume that the analytical expression of the characteristic polynomial of the system is available and give information with regard to the position of the roots of the characteristic polynomial in the left- or the right-half complex plane. Examples of such algebraic criteria are the Routh criterion, the Hurwitz criterion, and the continued fraction expansion criterion. These criteria are simple to apply and, for this reason, they have become most popular in studying the stability of linear systems.
Figure 6.2
instability.
Types of system description and their corresponding definitions of asymptotic stability, marginal stability, and
244 Chapter 6
Stability
2.
3. 4.
5.
6.
245
The Nyquist criterion: this criterion refers to the stability of the closed-loop systems and is based on the Nyquist diagram of the open-loop transfer function. The Bode criterion: this criterion is essentialy the Nyquist criterion extended to the Bode diagrams of the open-loop transfer function. The Nichols criterion: this criterion, as in the case of the Bode criterion, is essentially an extension of the Nyqist criterion to the Nichols diagrams of the open-loop transfer function. The root locus: this method consists of determining the root loci of the characteristic polynomial of the closed-loop system when one or more parameters of the system vary (usually these parameters are gain constants of the system). The Lyapunov criterion: this criterion is based on the properties of Lyapunov functions of a system and may be applied to both linear and nonlinear systems.
The algebraic criteria, the Nyquist criterion, the Bode criterion, and the Nichols criterion, as well as the root locus technique, are all criteria in the frequency domain. The Lyapunov criterion is in the time domain. The algebraic criteria and the Lyapunov criterion are presented in this chapter. The root locus technique is presented in Chap. 7 and the Nyquist, the Bode, and the Nichols criteria are presented in Chap. 8. 6.4 6.4.1
ALGEBRAIC STABILITY CRITERIA Introductory Remarks
The most popular algebraic criteria are the Routh, Hurwitz, and the continued fraction expansion criteria. The main characteristic of these three algebraic criteria is that they determine whether or not a system is stable by using a very simple numerical procedure, which circumvents the need for determining the roots of the characteristic polynomial. Consider the characteristic polynomial pðsÞ ¼ an sn þ an1 sn1 þ þ a1 s þ a0
ð6:4-1Þ
where the coefficients an ; an1 ; . . . ; a0 are real numbers. Here, we assume a0 6¼ 0 to avoid having a root at the origin. Next, we state the following well-known theorem of algebra. Theorem 6.4.1 The polynomial pðsÞ has one or more roots in the right-half complex plane if at least one of its coefficients is zero and/or all coefficients do not have the same sign. Theorem 6.4.1 is very useful since it allows one to determine the stability of a system by simply inspecting the characteristic polynomial. However, Theorem 6.4.1 gives only necessary stability conditions. This means that if pðsÞ satisfies Theorem 6.4.1, then the system with characteristic polynomial pðsÞ is definitely unstable. For the cases where pðsÞ does not satisfy Theorem 6.4.1, i.e., none of the coefficients of pðsÞ is zero and all its coefficients have the same sign, we cannot conclude as to the
246
Chapter 6
stability of the system. For these cases, we apply one of the algebraic criteria (Routh, or Hurwitz, or continued fraction expansion), which are presented below. 6.4.2
The Routh Criterion
The Routh criterion determines the number of the roots of the characteristic polynomial pðsÞ which lie in the right-half complex plane. This criterion is applied by using the Routh array, as shown in Table 6.1. In the Routh array, the elements an ; an1 ; an2 ; . . . ; a1 ; a0 are the coefficients of pðsÞ. The elements b1 ; b2 ; b3 ; . . . ; c1 ; c2 ; c3 ; . . . ; etc., are computed as follows: an an2 an an4 an1 an3 an1 an5 b1 ¼ ; b2 ¼ ;... ð6:4-2aÞ an1 an1
c1 ¼
an3 b2
an1 b1 b1
;
c2 ¼
an5 b3
an1 b1 b1
;...
ð6:4-2bÞ
and so on. The Routh criterion is given by the following theorem. Theorem 6.4.2 The necessary and sufficient conditions for Rei < 0, i ¼ 1; 2; . . . ; n, where 1 ; 2 ; . . . ; n are the roots of the characteristic polynomial pðsÞ, are that the first column of the Routh array does not involve any sign changes. In cases where it involves sign changes, then the system is unstable and the number of roots of pðsÞ with positive real part is equal to the number of sign changes. Example 6.4.1 Investigate the stability pðsÞ ¼ s3 þ 10s2 þ 11s þ 6.
Table 6.1
The Routh Array
n
an
an2
an4
n1
an1
an3
an5
sn2
b1
b3
b3
n3
c1
c2
c3
.. .
.. . .. . .. .
.. .
.. .
.. .. .. ...
s s
s
s1 s0
of
a
system
with
characteristic
polynomial
Stability
247
Solution Construct s3 s2 s1 s0
the Routh array as follows: 1
11
10 52=5
6 0
6
0
Since the first column of the Routh array involves no sign changes, it follows that the system is stable. Example 6.4.2 Investigate the stability pðsÞ ¼ s4 þ s3 þ s2 þ 2s þ 1.
of
a
system
with
characteristic
polynomial
Solution Construct the Routh array as follows: 1 1 1 s4 3 1 2 0 s 2 s 1 1 0 3 0 0 s1 0 1 0 0 s Since the first column of the Routh array involves two sign changes, it follows that pðsÞ has two roots in the right-half complex plane and therefore the system is unstable. It has been proven that we can multiply or divide a column or a row in the Routh array by a constant without influencing the end results of the Routh criterion. We may take advantage of this fact to simplify several operations which are required in constructing the Routh array. There are two cases in which the Routh criterion, as it has been presented above, cannot be applied. For these two cases certain modifications are necessary so that the above procedure is applicable. These two cases are the following. 1
A Zero Element in the First Column of the Routh Array
In this case the Routh array cannot be completed because the element below the zero element in the first column will become infinite as one applies relation (6.4-2). To circumvent this difficulty, we multiply the characteristic polynomial pðsÞ with a factor ðs þ aÞ, where a > 0 and a is not a root of pðsÞ. The conclusions regarding the stability of the new polynomial p^ ðsÞ ¼ ðs þ aÞpðsÞ are obviously the same as those of the original polynomial pðsÞ. Example 6.4.3 Investigate the stability of pðsÞ ¼ s4 þ s3 þ 2s2 þ 2s þ 3.
a
system
with
characteristic
polynomial
248
Chapter 6
Solution Construct the Routh array as follows: s4 1 2 3 s3 1 2 0 s2 0 3 0 s1 1 s0 Since the third element in the first column of the Routh array is zero, it is clear that the Routh array cannot be completed. If we multiply pðsÞ by the factor ðs þ 1Þ, we have p^ ðsÞ ¼ ðs þ 1ÞpðsÞ ¼ s5 þ 2s4 þ 3s3 þ 4s2 þ 5s þ 3 Next, construct the Routh array of p^ ðsÞ: 1 3 5 s5 2 4 3 s4 1 3:5 0 s3 3 0 s2 3 1 4:5 0 0 s 3 0 0 s0 According to the above Routh array, one observes that the polynomials p^ ðsÞ and pðsÞ have two roots in the right-half complex plane, and therefore the system with characteristic polynomial pðsÞ is unstable. 2
A Zero Row in the Routh Array
In this case the Routh array cannot be completed, because in computing the rest of the elements that follow the zero row, according to formula (6.4-2), the indeterminate form 0/0 will appear. To circumvent this difficulty, we proceed as follows: 1. 2. 3.
Form the ‘‘auxiliary polynomial’’ qðsÞ of the row which precedes the zero row. Take the derivative of qðsÞ and replace the zero row with the coefficients of qð1Þ ðsÞ, where qð1Þ ðsÞ is the derivative of qðsÞ: Complete the construction of the Routh array in the usual manner.
Example 6.4.4 Investigate the stability of a pðsÞ ¼ s5 þ s4 þ 2s3 þ 2s2 þ 3s þ 3. Solution Construct s5 s4 s3 s2 s1 s0
the Routh array as follows: 1 1 0 ?
2 3 2 3 0 0 ? ?
system
with
characteristic
polynomial
Stability
249
Since the row s3 of the Routh array involves only zeros, it is clear that the Routh array cannot be completed. At this point we construct the ‘‘auxiliary polynomial’’ qðsÞ ¼ s4 þ 2s2 þ 3 of the row s4 . Taking the derivative of qðsÞ yields qð1Þ ðsÞ ¼ 4s3 þ 4s. Next, form the new row s3 of the Routh array using the coefficients of qð1Þ ðsÞ and, subsequently, complete the Routh array in the usual manner to yield 1 2 3 s5 4 1 2 3 s 3 4 4 0 s 2 1 3 0 s 1 8 0 0 s 0 3 0 0 s Since the first column of the new Routh array appears to have two sign changes, it follows that p(s) has two roots in the right-half complex plane and therefore the system is unstable. Finally, consider the case where pðsÞ involves free parameters. Then the Routh criterion can be used to determine the appropriate range of values of these free parameters which guarantee stability of the system. This can be accomplished if one imposes the restriction that all the free parameters appearing in pðsÞ be such that all the coefficients of the elements of the first column in the Routh array have the same sign. This leads to a system of algebraic inequalities whose solution determines the range of values of the free parameters for which the system is stable. Example 6.4.5 Determine the range of values of the free parameter K such that the system with characteristic polynomial pðsÞ ¼ s3 þ 10s2 þ 11s þ K is stable. Solution Construct the Routh array: 1 11 s3 s2 10 K 110 K s1 0 10 0 s K 0 For the system to be stable all elements of the first column of the Routh array must have the same sign. Hence, there must be ð110 KÞ=10 > 0 and K > 0. The two inequalities are simultaneously satisfied for 0 < K < 110. Hence the system is stable when 0 < K < 110. 6.4.3
The Hurwitz Criterion
The Hurwitz criterion determines whether or not the characteristic polynomial has roots in the right-half complex plane. However, compared with the Routh criterion, it does not give any information regarding the number of the roots that the characteristic polynomial has in the right-half complex plane. The Hurwitz criterion is applied on the basis of the Hurwitz determinants, which are defined as follows:
250
Chapter 6
0 ¼ an 1 ¼ an1 an1 2 ¼ a n an1 3 ¼ an 0
an3 a n2
an3 an2 an1
an5 an4 an3
.. .
a n1 an n ¼ 0 0 . . . 0
an3 an2
a0 a1 a1 a0
an1 an .. . 0
if n if n if n if n an3 an2 .. . 0
odd even odd even .. .. .. ...
is is is is
0
0
0
0 0 0 .. .
an
The Hurwitz criterion is given by the following theorem. Theorem 6.4.3 The necessary and sufficient conditions for Re i < 0, i ¼ 0; 1; 2; . . . ; n, where 1 ; 2 ; . . . ; n are the roots of the characteristic polynomial pðsÞ, are that i > 0, for all i ¼ 0; 1; 2; . . . ; n. Example 6.4.6 Investigate the stability pðsÞ ¼ s3 þ 10s2 þ 11s þ 6.
of
a
system
with
characteristic
polynomial
Solution Compute the Hurtwitz determinants: 0 ¼ 1; 10 3 ¼ 1 0
10 1 ¼ 10; ; 2 ¼ 1 6 0 11 0 ¼ 624 10 6
6 ¼ 104; 11
Since all determinants are positive, it follows that the system is stable. 6.4.4
The Continued Fraction Expansion Criterion
The continued fraction expansion criterion, as in the case of the Hurwitz criterion, determines whether or not the characteristic polynomial has roots in the right-half
Stability
251
complex plane. To apply the continued fraction expansion criterion, the characteristic polynomial pðsÞ is first grouped into two polynomials p1 ðsÞ and p2 ðsÞ as follows: p1 ðsÞ ¼ an sn þ an2 sn2 þ an4 sn4 þ p2 ðsÞ ¼ an1 sn1 þ an3 sn3 þ an5 sn5 þ Next, we examine the ratio of p1 ðsÞ divided by p2 ðsÞ by expanding it as follows: p1 ðsÞ ¼ h1 s þ p2 ðsÞ h2 s þ
1 1 h3 s þ
..
1 .
1 hn s
The continued fraction expansion criterion is given by the following theorem. Theorem 6.4.4 If hj > 0, for all j ¼ 1; 2; . . . ; n, then Re j < 0, j ¼ 1; 2; . . . ; n, where 1 ; 2 ; . . . ; n are the roots of the characteristic polynomial pðsÞ and vice versa. Example 6.4.7 Investigate the stability pðsÞ ¼ s3 þ 10s2 þ 11s þ 6.
of
a
system
with
characteristic
polynomial
Solution Construct the polynomials p1 ðsÞ and p2 ðsÞ: p1 ðsÞ ¼ s2 þ 11s;
p2 ðsÞ ¼ 10s2 þ 6
We have 104 s p1 ðsÞ s3 þ 11s 1 1 1 10 s þ ¼ ¼ ¼ sþ 2 2 100 1 p2 ðsÞ 10s þ 6 10 10s þ 6 10 sþ 104 104 s 60 Therefore h1 ¼
1 ; 10
h2 ¼
100 ; 104
h3 ¼
104 60
Since all coefficients of the continued fraction expansion are positive, it follows that the system is stable. 6.4.5
Stability of Practical Control Systems
In what follows, we will present several practical automatic control system examples, investigating their stability using one of the algebraic criteria which we have just presented.
252
Chapter 6
Example 6.4.8 For the closed-loop position control system of Example 4.7.3 presented in Chap. 4, determine the range of values of the parameter K for which the closed-loop system is stable. Solution The transfer function of the closed-loop system of the Example 4.7.3 is GðsÞ ¼ HðsÞ ¼ 1 þ GðsÞ
K K sðs þ 2Þ ¼ K sðs þ 2Þ þ K 1þ sðs þ 2Þ
The characteristic polynomial pðsÞ of the closed-loop system is pðsÞ ¼ sðs þ 2Þ þ K ¼ s2 þ 2s þ K Construct s2 s1 s0
the Routh array of the characteristic polynomial: 1 2 K
K 0 0
Therefore, for the closed-loop system to be stable, K > 0. Example 6.4.9 Consider the closed-loop speed control system of Example 3.13.3 presented in Chap. 3 and assume that La ffi 0. The transfer function of the closed-loop system is given by ; B m ; Km ; Kb and relation (3.13-17). For simplicity, choose all parameters Lf ; Rf ; Jm Kt to be equal to unity and let K ¼ Kt Ka Kg Km N. Determine the range of values of the parameter K for which the closed-loop system is stable. Solution The transfer function of the closed-loop system is HðsÞ ¼
Ka Kg Km N Ka Kg Km N s þ R B þ K K Þ þ K K K K N ¼ ðs þ 1Þðs þ 2Þ þ K ðLf s þ Rf ÞðRa Jm a m m b t a g m
Therefore, the characteristic polynomial pðsÞ of the closed-loop system is pðsÞ ¼ s2 þ 3s þ K þ 2 Construct the Routh array of the characteristic polynomial: 1 K þ2 s2 3 0 s1 0 s0 K þ 2 For the closed-loop system to be stable, K þ 2 > 0 or K > 2. Example 6.4.10 This example refers to an automatic depth control system for submarines. In Figure 6.3 the block diagram of the closed-loop system is given, where the submarine is
Stability
Figure 6.3
253
Block diagram of the automatic depth control system of a submarine.
approximated by a second-order transfer function. The depth of the submarine is measured by a depth sensor with transfer function Fd ðsÞ. It is remarked that, as the value of the gain K of the controller is increased, so does the speed of sinking of the submarine. For simplicity, let Fd ðsÞ ¼ 1. Determine the range of values of K for which the closed-loop system is stable. Solution The transfer function of the closed-loop system is # " K ðs þ 0:3Þ2 s ðs2 þ 0:01Þ Gc ðsÞGs ðsÞ # HðsÞ ¼ ¼ " 1 þ Gc ðsÞGs ðsÞFd ðsÞ K ðs þ 0:3Þ2 1þ s ðs2 þ 0:01Þ ¼
Kðs þ 0:3Þ2 sðs2 þ 0:01Þ þ Kðs þ 0:3Þ2
The characteristic polynomial pðsÞ of the closed-loop system is pðsÞ ¼ sðs2 þ 0:01Þ þ Kðs þ 0:3Þ2 ¼ s3 þ Ks2 þ ð0:01 þ 0:6KÞs þ 0:09K Construct s3 s2 s1 s0
the Routh array of the characteristic polynomial: 1 K 0:6K 0:08 0:09K
0:01 þ 0:6K 0:09K 0
For the closed-loop system to be stable, the two inequalities 0:06K 0:08 > 0 and 0:09K > 0 must hold simultaneously. This holds true for K > 0:1333: Example 6.4.11 This example refers to the stabilization of ships due to oscillations resulting from waves and strong winds, presented in paragraph 12 of Sec. 1.4 and shown in Figure 1.20. When a ship exhibits a deviation of degrees from the vertical axis, as shown in Figure 1.20, then most ships use fins to generate an opposite torque which restores
254
Chapter 6
Figure 6.4
A simplified block diagram of ship stabilization control system.
the ship to the vertical position. In Figure 6.4 the block diagram of the system is given, where, obviously, r ¼ 0 is the desired deviation of the ship. The length of the fins projecting into the water is controlled by an actuator with transfer function Gc ðsÞ ¼ K=s. The deviation from the vertical axis is measured by a measuring device with transfer function Fd ðsÞ ¼ F0 ¼ constant. A simplified mathematical description of the ship is given by the second-order transfer function Gs ðsÞ. Typical values of and !n in Gs ðsÞ are ¼ 0:1 and !n ¼ 2. For simplicity, let F0 ¼ 1. Determine the range of values of K for which the closed-loop system is stable. It is noted that since it is desirable that r ¼ 0, the problem of restoring the ship to its vertical position is a typical regulator problem (see Sec. 11.3). Solution The transfer function of the closed-loop system is given by K 4 Gc ðsÞGs ðsÞ 4K s s2 þ 0:4s þ 4 ¼ 3 ¼ HðsÞ ¼ 2 K 4 1 þ Gc ðsÞGs ðsÞFd ðsÞ s þ 0:4s þ 4s þ 4K 1þ s s2 þ 0:4s þ 4 The characteristic polynomial pðsÞ of the transfer function of the closed-loop system is the following: pðsÞ ¼ s3 þ 0:4s2 þ 4s þ 4K Construct s3 s2 s1 s0
the Routh array of the characteristic polynomial: 1 0:4 4 10K 4K
4 4K 0
For the closed-loop system to be stable, the inequalities 4 10K > 0 and K > 0 must hold simultaneously. This holds true for 0 < K < 0:4.
Stability
255
Example 6.4.12 This example refers to the problem of controlling the yaw of a fighter jet (Figure 6.5a). A simplified diagram of the closed-loop system is given in Figure 6.5b, where the aircraft is approximated by a fourth-order system. Determine the range of values of K for which the closed-loop system is stable. Solution The transfer function of the closed-loop system is given by K Gc ðsÞGa ðsÞ K sðs þ 2Þðs2 þ s þ 1Þ HðsÞ ¼ ¼ ¼ K 1 þ Gc ðsÞGa ðsÞ sðs þ 2Þðs2 þ s þ 1Þ þ K 1þ sðs þ 2Þðs2 þ s þ 1Þ The characteristic polynomial pðsÞ of the transfer function of the closed-loop system is the following: pðsÞ ¼ s4 þ 3s2 þ 3s2 þ 2s þ K Construct the Routh array of the characteristic polynomial:
Figure 6.5
Closed-loop system for the control of the yaw of a fighter aircraft. (a) A fighter aircraft; (b) simplified block diagram of the closed-loop system.
256
Chapter 6
s4 s3 s2 s1 s0
1
3
K
3 7=3
2 K
0
2 9K=7
0
K
For the closed-loop system to be stable, the inequalities 2 9K=7 > 0 and K > 0 must hold simultaneously. This holds true for 0 < K < 14=9: Example 6.4.13 One of the most important applications of industrial robots is that of welding. Many such robots use a vision system to measure the performance of the welding. Figure 6.6 shows a simplified block diagram of such a system. The welding process is approximated by a second-order underdamped system, the vision system by a unity transfer function, and the controller is assumed to be of the integrator type. Determine the range of values of K for which the closed-loop system is stable. Solution The transfer function of the closed-loop system is Gc ðsÞGw ðsÞ HðsÞ ¼ ¼ 1 þ Gc ðsÞGw ðsÞFv ðsÞ
K K sðs þ 3Þðs þ 4Þ ¼ K sðs þ 3Þðs þ 4Þ þ K 1þ sðs þ 3Þðs þ 4Þ
The characteristic polynomial pðsÞ of the transfer function of the closed-loop system is the following: pðsÞ ¼ s3 þ 7s2 þ 12s þ K Construct the Routh array of the characteristic polynomial:
Figure 6.6
Simplified closed-loop block diagram for the control of a welding robot.
Stability
257
s3
1
12
s
2
s
7 84 K 7 K
K
1
s0
0
For the closed-loop system to be stable, the inequalities ð84 KÞ=7 > 0 and K > 0 must hold simultaneously. This holds true for 0 < K < 84. Example 6.4.14 In digital computers, large disk-storage devices are widely used today. As the disk is spinning, the data head is moved to various positions. This movement must be made very fast and very accurately. A simplified block diagram of the closed-loop headposition control system is given in Figure 6.7. The mathematical model of the head is approximated by a third-order system and the particular controller applied is of the phase lead or lag type (see Chap. 9), depending on the parameter . Determine: For ¼ 3, the range of values of K for which the closed-loop system is stable. (b) For arbitrary , the ranges of both and K for which the closed-loop system is stable.
(a)
Solution (a) For ¼ 3, the transfer function of the closed-loop system is given by ðs þ 3Þ 1 K Gc ðsÞGh ðsÞ ðs þ 1Þ sðs þ 2Þðs þ 5Þ HðsÞ ¼ ¼ ðs þ 3Þ 1 1 þ Gc ðsÞGh ðsÞ 1þK ðs þ 1Þ sðs þ 2Þðs þ 5Þ Kðs þ 3Þ ¼ sðs þ 1Þðs þ 2Þðs þ 5Þ þ Kðs þ 3Þ The characteristic polynomial pðsÞ of the transfer function of the closed-loop system is the following: pðsÞ ¼ s4 þ 8s2 þ 17s2 þ ðK þ 10Þs þ 3K
Figure 6.7
Simplified closed-loop block diagram for head-position control.
258
Chapter 6
Construct the Routh array of the characteristic polynomial: s4 s3 s2 s1 s0
1 8 126 K 8 K 2 76K þ 1260 126 K 3K
17 K þ 10
3K 0
3K 0
For the closed-loop system to be stable, the inequalities ð126 KÞ=8 > 0, K 2 76K þ 1260 > 0 and 3K > 0 must hold simultaneously, i.e., there must hold K < 126, 90 < K < 14, and K > 0. This holds true for 0 < K < 14. (b) For arbitrary , the transfer function of the closed-loop system is given by Kðs þ Þ sðs þ 1Þðs þ 2Þðs þ 5Þ Kðs þ Þ 1þ sðs þ 1Þðs þ 2Þðs þ 5Þ Kðs þ Þ ¼ sðs þ 1Þðs þ 2Þðs þ 5Þ þ Kðs þ Þ
Gc ðsÞGh ðsÞ ¼ HðsÞ ¼ 1 þ Gc ðsÞGh ðsÞ
The characteristic polynomial pðsÞ of the transfer function of the closed-loop system is the following: pðsÞ ¼ s4 þ 8s3 þ 17s2 þ ðK þ 10Þs þ K Construct the Routh array of the characteristic polynomial: s4
1
17
K
s3
8 126 K 8
K þ 10
0
s2 s1 s0
126 K ðK þ 10Þ 8K 8 126 K 8 K
K
0
For the closed-loop system to be stable, the following inequalities must hold simultaneously: 126 K >0 8 126 K ðK þ 10Þ 8K 8 >0 126 K 8 K > 0 The above inequalities may be written as
Stability
259
126 > K ðK þ 10Þð126 KÞ 64K > 0 K > 0 In Figure 6.8, the hatched area shows the range of values of and K for which the above inequalities are satisfied and, consequently, the closed-loop system is stable. Example 6.4.15 This example refers to the human respiratory control system. A simplified block diagram of the system is given in Figure 6.9. Our body has certain special chemoreceptors which measure the percentage of CO2 in the blood. This percentage is the output YðsÞ. The ventilation BðsÞ at the lungs is known to be proportional to the percentage of CO2 in the blood. That is, our body, by measuring YðsÞ, indirectly measures the ventilation BðsÞ at the lungs. Determine the range of values of K for which the closed-loop system is stable. Solution The transfer function of the closed-loop system is 0:25 ðs þ 0:5Þ2 ðs þ 0:1Þðs þ 10Þ 0:25K 1þ 2 ðs þ 0:5Þ ðs þ 0:1Þðs þ 10Þ 0:25 ¼ 4 s þ 11:1s3 þ 11:35s2 þ 3:525s þ 0:25ðK þ 1Þ
Gc ðsÞGr ðsÞ HðsÞ ¼ ¼ 1 þ KGc ðsÞGr ðsÞ
Construct the Routh array of the characteristic polynomial of the closed-loop system:
Figure 6.8
Range of values of and K for which the closed-loop system is stable.
260
Chapter 6
Figure 6.9 s4 s3 s2 s1 s0
Block diagram of the closed-loop humam respiratory control system.
1 11:1 11:032 3:273 0:252K 0:25ðK þ 1Þ
11:35 3:525 0:25ðK þ 1Þ 0
0:25ðK þ 1Þ 0
For the closed-loop system to be stable, the inequalities 3:273 0:252K > 0 and 0:25ðK þ 1Þ > 0 must simultaneously be satisfied. This holds true for 1 < K < 13. 6.5 6.5.1
STABILITY IN THE SENSE OF LYAPUNOV Introduction—Definitions
The final objective of this section is to derive the Lyapunov stability criterion for linear, time-invariant systems presented in Subsec. 6.5.4. To this end, we first present some preliminary results from the Lyapunov’s stability theory for nonlinear systems which are necessary for the derivation of the results sought in Subsec. 6.5.4. The stability results for nonlinear sytems are, by themselves, of great importance to control engineers. The Lyapunov approach is based on the differential equations which describe the system and gives information about the stability of the system without requiring the solution of the differential equations. The Lyapunov’s results may be grouped in two basic methods: the first method of Lyapunov (or the method of the first approximation) and the second method of Lyapunov (or the direct method). Before we present the two methods of Lyapunov, we first give some preliminary material and definitions necessary for the results that follow. To this end, consider a system described in state space via the mathematical model x ¼ fðx; tÞ; xðt Þ ¼ x ð6:5-1Þ 0
0
The solution of Eq. (6.5-1) is denoted by rðt; x0 ; t0 Þ. This solution depends not only upon x0 but also upon t0 . Then, the following identity holds: rðt0 ; x0 ; t0 Þ ¼ x0
ð6:5-2Þ
Definition 6.5.1 The vector xe is called an equilibrium state of system (6.5-1) if it satisfies the relation
Stability
261
fðxe ; tÞ ¼ 0;
ð6:5-3Þ
for all t
Obviously, for the determination of the equilibrium states, it is not necessary to solve the dynamic equations (6.5-1) but only the algebraic equations (6.5-3). For example, when the system (6.5-1) is linear time-invariant, i.e., fðx; tÞ ¼ Ax, then there exists only one equilibrium state when jAj 6¼ 0 and an infinite number of equilibrium states when jAj ¼ 0. When the system (6.5-1) is nonlinear, then one or more equilibrium states may exist. It is noted that each equilibrium state can be shifted to the origin by using an appropriate transformation, where the new equilibrium state will now satisfy the following condition: fð0; tÞ ¼ 0;
ð6:5-4Þ
for all t
We give the following definition of stability. Definition 6.5.2 The equilibrium state xe of system (6.5-1) is stable if, for every real number " > 0, there exists a real number ð"; t0 Þ > 0 such that, if kx0 xe k
ð6:5-5Þ
then krðt; x0 ; t0 Þ xe k ";
for all t
ð6:5-6Þ
If does not depend on t0 , then xe is uniformly stable. In Figure 6.10, an equilibrium state xe of a system with two variables is presented. The regions Sð"Þ and SðÞ are the interiors of two circles with their centers at xe and with radii " > 0 and > 0. The region Sð"Þ consists of all points which satisfy
Figure 6.10
A stable equilibrium state.
262
Chapter 6
the condition kx xe k ". In the figure it is shown that for every Sð"Þ there exists an SðÞ such that, starting with an initial state x0 which lies inside SðÞ, the trajectory rðt; x0 ; t0 Þ is contained within Sð"Þ. Definition 6.5.3 The solution rðt; x0 ; t0 Þ of system (6.5-1) is bounded if for > 0 there exists a constant "ð; t0 Þ such that, if kx0 xe k
ð6:5-7aÞ
then krðt; x0 ; t0 Þ xe k "ð; t0 Þ;
for all t t0
ð6:5-7bÞ
If " does not depend upon t0 , then the solution is uniformly bounded. Definition 6.5.4 An equilibrium state xe of system (6.5-1) is asymptotically stable if it is stable and if every solution with x0 sufficiently close to xe converges to xe as t increases. 6.5.2
The First Method of Lyapunov
The first method of Lyapunov, or the method of the first approximation, is based on the approximation of the nonlinear differential equation by a linearized differential equation. This approximation is performed for each equilibrium state separately, and conclusions about stability hold only for a small region around the particular equilibrium state. For this reason the first method of Lyapunov is of limited value. Consider the nonlinear system x ¼ fðxÞ ð6:5-8Þ and let xe be an equilibrium state. Expand Eq. (6.5-8) in Taylor series about the point x ¼ xe to yield T T 1 T @ @f x ¼ fðxÞ ¼ fðx Þ þ @f ðx xe Þ þ ðx xe Þ ðx xe Þ þ e @x x¼xe 2 @x @x x¼xe ¼ fðxe Þ þ Aðx xe Þ þ ½Bðx xe Þðx xe Þ þ where
2
3 f1 ðxÞ 6 f ðxÞ 7 6 2 7 7 fðxÞ ¼ 6 6 .. 7; 4 . 5 fn ðxÞ 2
@f1 6 @x1 6 6 @f 6 2 T 6 @f @x1 A¼ ¼6 @x x¼xe 6 6 .. 6 . 6 4 @fn @x1
@f1 @x2 @f2 @x2 .. . @fn @x2
ð6:5-9Þ
3 @f1 @xn 7 7 @f2 7 7 7 @xn 7 7 .. 7 . 7 7 @fn 5 @xn
x¼xe
The matrix Bðx xe Þ involves higher-order terms. Since xe is an equilibrium point, it follows that fðxe Þ ¼ 0. If we let z ¼ x xe , then Eq. (6.5-9) can be written as follows: z ¼ Az þ BðzÞz þ ð6:5-10Þ
Stability
263
The first approximation, that is the linear part of (6.5-10), is the following: z ¼ Az ð6:5-11Þ The first method of Lyapunov is based on the following theorem. Theorem 6.5.1 If all the eigenvalues of the matrix A have nonzero real parts, then the conclusions about the stability of the nonlinear system in the neighborhood of xe may be derived from the study of the stability of the linear system (6.5-11). Thus, the first method of Lyapunov reduces the problem of studying the stability of nonlinear systems to well-established methods for studying the stability of linear systems. 6.5.3
The Second Method of Lyapunov
The second or direct method of Lyapunov is based on the following idea: if a system has a stable equilibrium state xe , then the total energy stored in the system decays as time t increases, until this total energy reaches its minimum value in the equilibrium state xe . The determination of the stability of a linear or nonlinear system via the second method of Lyapunov requires the determination of a special scalar function, which is called the Lyapunov function. We give the following definition. Definition 6.5.5 The time-invariant Lyapunov function, designated by VðxÞ, satisfies the following conditions for all t1 > t0 and for all x in the neighborhood of x ¼ 0, where x ¼ 0 is an equilibrium point: 1. 2. 3. 4.
VðxÞ and its partial derivatives are defined and they are continuous Vð0Þ ¼ 0 VðxÞ > 0, for all x 6¼ 0 V_ ðxÞ < 0, for all x 6¼ 0, where V_ ðxÞ is the total derivative of VðxÞ, i.e., V_ ðxÞ ¼ ½gradx VT x
The second method of Lyapunov is based on the following theorem. Theorem 6.5.2 Consider the system x ¼ fðx; tÞ; fð0; tÞ ¼ 0
ð6:5-12Þ
Assume that a Lyapunov function VðxÞ can be determined for this system. Then, the equilibrium state x ¼ 0 is asymptotically stable and the system (6.5-12) is said to be stable in the sense of Lyapunov. 6.5.4
The Special Case of Linear Time-Invariant Systems
From Theorem 6.5.2 it follows that the problem of studying the stability of a system using the second method of Lyapunov is one of determining a Lyapunov function for the particular system. This function may not be unique, while its determination presents great difficulties. It is noted that in cases where we cannot determine even one Lyapunov function for a particular system, this simply means that we cannot
264
Chapter 6
conclude about the stability of the system and not that the system is unstable. In what follows we will restrict our presentation to the determination of Lyapunov functions for the special case of linear time-invariant systems. For other types of system, e.g., time-varying, non-linear, etc., see [2–4] and [12]. For the case of time-invariant systems, the following theorem holds. Theorem 6.5.3 Consider the linear time invariant system x ¼ Ax, with jAj 6¼ 0 and xe ¼ 0. Also consider the scalar function VðxÞ ¼ xT Px, where P is a positive definite real symmetric matrix. Then, VðxÞ ¼ xT Px is a Lyapunov function of the system if, and only if, for any positive definite real symmetric matrix Q there exists a positive definite real symmetric matrix P such that the following relation holds: AT P þ PA ¼ Q
ð6:5-13Þ
Proof Taking the derivative of VðxÞ ¼ xT Px with respect to time yields VðxÞ ¼ x T Px þ xT P x Upon using the relation x ¼ Ax, the expression for VðxÞ becomes
ð6:5-14Þ
VðxÞ ¼ xT AT Px þ xT PAx
ð6:5-15Þ According to Definition 6.5.5, Condition 4, there must be VðxÞ < 0, for all x 6¼ 0. Hence, Condition 4 is satisfied if we set VðxÞ ¼ xT AT Px þ xT PAx ¼ xT Qx ð6:5-16Þ where the right-hand side term xT Qx < 0 due to the choice of Q. For eq. (6.5-16) to hold, the matrices P and A must satisfy the following relation: AT P þ PA ¼ Q
ð6:6-17Þ
which is the same with relation (6.5-13). Example 6.5.1 Consider the linear system x ¼ Ax, where 0 1 A¼ 2 3 Determine the Lyapunov function for the system. Solution Consider the relation (6.5-13) where, for simplicity, let Q ¼ I. Then we have 0 2 p11 p12 p 0 1 1 0 p12 þ 11 ¼ 1 3 p12 p22 p12 p22 2 3 0 1 where use was made of the relation p21 ¼ p12 , since we have assumed that the matrix P is symmetric. The above equation, due to the symmetry in P and Q, yields the following nðn þ 1Þ=2 ¼ 3 algebraic equations:
Stability
265
4p12 ¼ 1 p11 3p12 2p22 ¼ 0 2p12 6p22 ¼ 1 The above equations give the following matrix 5 1 P ¼ 41 41 4
4
If we apply the Sylvester’s criterion (Sec. 2.12), it follows that the matrix P is positive definite. Therefore, the sytem is asymptotically stable. The Lyapunov function is " # 5 1 x1 T 4 4 VðxÞ ¼ x Px ¼ ½ x1 x2 1 1 ¼ 14 ½5x21 þ 2x1 x2 þ x22 x2 4 4 To check the results, we investigate VðxÞ using Definition 6.5.5. It is clear that VðxÞ satisfies the first three conditions of Definition 6.5.5. For the fourth condition we compute V_ ðxÞ to yield V_ ðxÞ ¼ ½gradx VT x ¼ ½gradx VT Ax ¼ 12 ½10x1 þ 2x2 ; 2x1 þ 2x2 Ax " # x1 ¼ 14 ½4x1 4x2 ; 4x1 4x2 ¼ x21 x22 x2 Clearly, VðxÞ also satisfies the fourth condition of Definition 6.5.5. Hence, VðxÞ is a Lyapunov function and therefore the system is asymptotically stable. PROBLEMS 1. Investigate the stability of the systems having the following characteristic polynomials: (a) s4 þ 2s3 þ 6s2 þ 7s þ 5 (b) s3 þ s2 þ 2s þ 1 (c) s3 þ s2 þ 1 (d) s4 þ s3 þ s2 þ 2s þ 4 (e) 2s4 þ s3 þ 3s2 þ 5s þ 10
(f) (g) (h) (i) (j)
s5 þ 3s4 þ 2s3 þ 6s2 þ 6s þ 9 s4 þ 2s3 þ 3s2 þ 4s þ 5 s5 þ s4 þ 2s3 þ 2s2 þ 3s þ 4 s5 þ s4 þ s3 þ 2s2 þ 2s þ 2 s4 þ 3s3 þ 4s2 þ 3s þ 3
2. Find the range of values of the parameter K for which the systems, with the following characteristic polynomials, are stable: (a) s3 þ s2 þ Ks þ 1 (b) s4 þ s3 þ 2s2 þ 3s þ K (c) s4 þ ðK þ 1Þs3 þ s2 þ 5s þ 2 3. The block diagram of a system for the speed control of a tape drive is shown in Figure 6.11. Find the range of values of K so that the closed-loop system is stable. 4. Consider a rocket altitude control system having the block diagram shown in Figure 6.12. (a) Given that the transfer function of the controller is
266
Chapter 6
Figure 6.11
5.
6.
7.
8.
Gc ðsÞ ¼ ðs þ 3Þðs þ 2Þ=s, determine the range of values of K for which the closedloop system is stable. (b) Given that the transfer function of the controller is Gc ¼ s þ a, determine the range of values of K and a for which the closed-loop system is stable. The block diagram of a metal sheet thickness control system (depicted in Figure 1.10) is given in Figure 6.13. Find the range of values of K and a such that the closed-loop system is stable. The block diagram of a feedback control system is shown in Figure 6.14. (a) Determine the range of values of K2 so that the system is stable for K1 ¼ K3 ¼ 1, T1 ¼ 1, T3 ¼ 1=2, and T4 ¼ 1=3. (b) Determine the range of values of the parameter T4 for which the system is stable, given that K1 ¼ 1, K2 ¼ 2, K3 ¼ 5, T1 ¼ 1=2, and T3 ¼ 1=3. Consider a satellite orientation control system shown in Figure 3.56 of Example 3.13.7, where Kb Kt ¼ 1 and J ¼ 1. Let the transfer function of the controller be Gs ðsÞ ¼ Kp þ ðKi =sÞ þ Kd s (PID controller). (a) Find the range of values of the controller parameters so that the closed-loop system is stable. (b) For Kp ¼ 1, Ki ¼ 2, and Kd ¼ 1, determine the number of the closed-loop poles located in the right-half complex plane. The closed-loop control system of an aircraft wing is given in Figure 6.15. Determine the range of values of the parameters K and T of the hydrualic servomotor that guarantee the stability of the closed-loop system.
Figure 6.12
Stability
Figure 6.13
Figure 6.14
Figure 6.15
267
268
Chapter 6
9. The differential equation which describes the dynamics of the pendulum shown in Figure 6.16 is the following mR2 € þ K _ þ mgR sin ¼ 0 where K is the friction coefficient, m is the mass at the end of the rod, R is the length of the pendulum, g is the gravitational constant, and is the angle of the pendulum from the vertical axis. for this system, (a) find a state-space model and determine the equilibrium states and (b) investigate the stability of the equilibrium states by means of the first method of Lyapunov. 10. Carry out the study of stability of the following nonlinear systems using the corresponding candidate Lyapunov functions: x_ 1 ¼ 2x1 þ 2x42 ; x_ 2 ¼ x2 ; and VðxÞ ¼ 6x21 þ 12x22 þ 4x1 x42 þ x82 (b) x_ 1 ¼ x1 þ x2 þ x1 ðx21 þ x22 Þ; x_ 2 ¼ x1 x2 þ x2 ðx21 þ x22 Þ; (a)
VðxÞ ¼ (c)
x_ 1 ¼
x21
þ
6x1 þ 2x2 ; ð1 þ x21 Þ2
VðxÞ ¼
and
x22 x_ 2 ¼
2x1 2x2 ; 2 2 ð1 þ x1 Þ ð1 þ x21 Þ2
and
x21 þ x22 1 þ x21
11. For the system described by the equation x ¼ Ax, where a 0 A¼ 1 1 determine the range of values of the parameter a so that the system is asymptotically stable.
Figure 6.16
Stability
269
12. Determine a Lyapunov function for the systems of the form x ¼ Ax, where A¼
1 1
2
0 6 A¼4 0 2
2
2 ; 4 1
0 6 A ¼ 4 3 0
3
7 0 1 5; 5 4
1 0 2
0 A¼
3 0 7 1 5; 1
1
1
2
3
BIBLIOGRAPHY Books 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.
PJ Antsaklis, AN Michel. Linear Systems. New York: McGraw-Hill, 1997. DP Atherton. Nonlinear Control Engineering. London: Van Nostrand Reinhold, 1975. A Blaguiere. Nonlinear System Analysis. New York: Academic Press, 1966. PA Cook. Nonlinear Dynamical Systems. London: Prentice-Hall, 1986. JJ D’Azzo, CH Houpis. Linear Control System Analysis and Design, Conventional and Modern. New York: McGraw-Hill, 1975. PM DeRusso, RJ Roy, CM Close. State Variables for Engineers. New York: John Wiley, 1965. JJ DiStefano III, AR Stubberud, IJ Williams. Feedback and Control Systems. Schaum’s Outline Series. New York: McGraw-Hill, 1967. RC Dorf, RE Bishop. Modern Control Analysis. London: Addison-Wesley, 1995. JC Doyle. Feedback Control Theory. New York: Macmillan, 1992. GF Franklin, JD Powell, A Emami-Naeini. Feedback Control of Dynamic Systems. Reading, MA: Addison-Wesley, 1986. B Friedland. Control System Design. An Introduction to State-space Methods. New York: McGraw-Hill, 1987. JE Gibson. Nonlinear Automatic Control. New York: McGraw-Hill, 1963. MJ Holtzman. Nonlinear System theory, a Functional Analysis Approach. Englewood Cliffs, New Jersey: Prentice Hall, 1970. M Krstic, I Kanellakopoulos, P Kokotovic. Nonlinear and Adaptive Control Design. New York: John Wiley, 1995. BC Kuo. Automatic Control Systems. London: Prentice Hall, 1995. NS Nise. Control Systems Engineering. New York: Benjamin and Cummings, 1995. K Ogata. Modern Control Systems. London: Prentice Hall, 1997. WJ Rugh. Linear System Theory. 2nd ed. Englewood Cliffs, New Jersey: Prentice Hall, 1996. DD Siljak. Nonlinear Systems, the Parameter Analysis and Design. New York: John Wiley, 1969. M Vidyasagar. Nonlinear Systems Analysis. 2nd ed. Englewood Cliffs, New Jersey: Prentice Hall, 1993. LA Zadeh, CA Desoer. Linear System Theory—The State Space Approach. New York: McGraw-Hill, 1963.
7 The Root Locus Method
7.1
INTRODUCTION
The positions of the poles of the transfer function of a system in the complex plane characterize completely the system’s stability and play a decisive role in the shape of its time response. For these two basic reasons, the determination of a system’s poles is a problem of special importance in control practice. One of the main problems in control systems is the design of an appropriate controller capable of shifting the poles of the open-loop system to new desired closed-loop pole positions in the complex plane. In its simplest form, such a controller is a gain constant K of an amplifier connected in series with the system’s openloop transfer function. Changing the value of the constant K, from 1 and þ1, results in shifting the poles of the closed-loop system in the complex plane. Specifically, the locus of the roots of the closed-loop system characteristic polynomial, which is formed in the s-plane as K varies, is the subject of this chapter. The development of a relatively simple method for constructing the root locus of the closed-loop characteristic polynomial is due to Evans [4, 8, 9]. This method gives an approximate graphical representation of the root locus which is very useful in the design of a closed-loop system since it gives the position of the poles of the closed-loop system in the s-plane for all values of the gain constant K.
7.2
INTRODUCTORY EXAMPLE
To facilitate the understanding of the root locus method, a simple introductory example will first be presented. Example 7.2.1 Consider the closed-loop position servomechanism system described in Subsec. 3.13.2. Let La ffi 0, Kp ¼ 1, A ¼ 1, and B ¼ 6. Then, the closed-loop system is simplified, as in Figure 7.1. For this simplified system, draw the root locus of the closedloop system, for K 2 ð1; þ1Þ. 271
272
Chapter 7
Figure 7.1
Simplified block diagram of the position control system described in Subsec.
3.13.2.
Solution The closed-loop system transfer function is given by HðsÞ ¼
GðsÞ K ¼ 1 þ GðsÞFðsÞ s2 þ 6s þ K
ð7:2-1Þ
The characteristic polynomial pc ðsÞ of the closed-loop system is pc ðsÞ ¼ s2 þ 6s þ K
ð7:2-2Þ
The roots of the characteristic polynomial pc ðsÞ, or equivalently the poles of the closed-loop system transfer function HðsÞ, are pffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffi s1 ¼ 3 þ 9 K and s2 ¼ 3 9 K ð7:2-3Þ It is clear that the roots s1 and s2 depend upon the parameter K. Therefore, as K varies, the two roots will vary as well. The diagrams of s1 and s2 in the complex plane, for K 2 ð1; þ1Þ, form the root locus of the characteristic polynomial pc ðsÞ. To draw the root locus of pc ðsÞ, we calculate the roots s1 and s2 while K changes from 1 to þ1. We observe the following: 1. 2. 3. 4. 5.
For For For For For
1 < K < 0, both roots are real with s1 > 0 and s2 < 0 K ¼ 0, s1 ¼ 0 and s2 < 0 0 < K < 9, both roots are negative K ¼ 9, we have the double root s1 ¼ s2 ¼ 3 9 < K < þ1, both roots are complex conjugates with real part 3.
The above remarks for the roots s1 and s2 suffice to determine their root locus. In Figures 7.2a and 7.2b, the root locus of s1 and s2 are shown, respectively. Usually, the root locus of all roots of a polynomial pc ðsÞ is given in one single figure. For this example, the root locus of both roots s1 and s2 of pc ðsÞ ¼ s2 þ 6s þ K is given in Figure 7.3. The motivation for constructing the root locus of the characteristic polynomial pc ðsÞ is that it reveals important information with regard to the behavior of the closed-loop system. The most important information is the following. 1
Stability
From the root locus of Figure 7.3, the stability of the closed-loop system may easily be studied. As already known, the closed-loop system is stable when both roots s1 and s2 are in the left-half complex plane, which occurs when K > 0. Therefore, the
The Root Locus Method
273
The root locus of s1 and s2 of the characteristic polynomial pc ðsÞ s2 þ p¼ ffiffiffiffiffiffiffiffiffiffiffiffi ffi 6s þ K of the position control system of Figure 7.1. (a) The root locus of s ¼ 3 þ 9 K ; (b) the 1 pffiffiffiffiffiffiffiffiffiffiffiffiffi root locus of s2 ¼ 3 9 K .
Figure 7.2
root locus technique can replace the stability criteria for linear time-invariant systems presented in Chap. 6. 2
Transient Response
The closed-loop system’s transient response depends mainly on the locations of the roots of pc ðsÞ in the complex plain (see Secs 4.2 and 4.3). This is demonstrated by the following two cases: For 0 < K 9, the system has two negative roots; therefore its response does not invovle any oscillations. b. For K > 9, the system has two complex roots; therefore its response involves oscillations. Furthermore, the system’s damped frequency !d increases as K increases. a.
We must keep in mind that the positions of the zeros of any transfer function also affect the transient response of the system.
Figure 7.3 7.1.
The root locus of pc ðsÞ ¼ s2 þ 6s þ K of the position control system of Figure
274
3
Chapter 7
Characteristics in the Frequency Domain
Since the bandwidth is proportional to the damped frequency !d (see Sec. 8.3), the root locus also gives information about the system’s bandwidth. For this example, as K increases, it is clear that the bandwidth also increases. The above observations are valuable in the study and design of control systems and are presented so as to further motivate the study of the root locus method. For the general case, where the characteristic polynomial pc ðsÞ is of higher order, the root locus construction method presented in the above example is difficult, if not impossible, to apply. This is mainly because for very high order polynomials there exists no method for determining the analytical expression of the roots of the polynomial as a function of its coefficients. For this reason, the construction of the root locus for the general case is not done directly, i.e., on the basis of the analytical expressions of the roots of pc ðsÞ, but indirectly. This chapter is devoted to the development of such a method, which, as already mentioned, was first introduced by Evans.
7.3 7.3.1
CONSTRUCTION METHOD OF ROOT LOCUS Definition of Root Locus
Here, we shall present a more mathematical definition of the root locus. To this end, consider the characteristic equation of any closed-loop system (see for example figure 7.1) given by the following relation: 1 þ GðsÞFðsÞ ¼ 0
ð7:3-1Þ
or equivalently by the relation GðsÞFðsÞ ¼ 1
ð7:2-2Þ
The characteristic equation (7.3-2) can also be written as two equations, involving the amplitude and the phase, as follows jGðsÞFðsÞj ¼ 1
ð7:3-3aÞ
and GðsÞFðsÞ ¼ ð2 þ 1Þ;
¼ 0; 1; 2; . . .
ð7:3-3bÞ
Assume that the open-loop transfer function GðsÞFðsÞ has the following general form: GðsÞFðsÞ ¼ K
ðs þ z1 Þðs þ z2 Þ ðs þ zm Þ sm þ d1 sm1 þ dm1 s þ dm ¼K n ðs þ p1 Þðs þ p2 Þ ðs þ pn Þ s þ b1 sn1 þ þ bn1 s þ bn ð7:3-4Þ
Hence, Eqs (7.3-3a) and (7.3-3b) will have the form
The Root Locus Method m Y
jKj i¼1 n Y
275
js þ zi j ¼ 1;
1 < K < þ1
ð7:3-5aÞ
js þ pi j
i¼1 m X
s þ zi
n X
i¼1
s þ pi ¼
ð2 þ 1Þ;
K>0
ð7:3-5bÞ
2 ;
K<0
ð7:3-5cÞ
i¼1
for ¼ 0; 1; 2; . . . Definition 7.3.1 The root locus of a closed-loop system with characteristic equation (7.3-1) is defined as the locus of all points s which satisfy Eqs (7.3-5a,b) for K 2 ð0; þ1Þ. The locus of points s which satisfy Eqs (7.3-5a,c) for K 2 ð1; 0Þ is called the complementary root locus.
7.3.2
Theorems for Constructing the Root Locus
In this section, several theorems will be presented which greatly facilitate the construction of the root locus. In particular, these theorems are the basic tools in approximately constructing the root locus. Theorem 7.3.1 (Starting or Departure Points) The points of the root locus for K ¼ 0 are the poles of GðsÞFðsÞ. These points are called starting or departure points of the root locus. Proof Relation (7.3-5a) may be written as n Y
js þ pi j ¼ jKj
i¼1
m Y
js þ zi j
ð7:3-6Þ
i¼1
For K ¼ 0, relation (7.3-6) gives n Y
js þ pi j ¼ 0
ð7:3-7Þ
i¼1
Relation (7.3-7) is satisfied for s ¼ pi . Therefore, the points of the root locus for K ¼ 0 are the poles p1 ; p2 ; . . . ; pn of GðsÞFðsÞ. Theorem 7.3.2 (Ending or Arrival Points) The points of the root locus for K ! 1 are the zeros of GðsÞFðsÞ. These points are called ending or arrival points of the root locus.
276
Chapter 7
Proof Relation (7.3-5a) may be written as m Y i¼1 n Y
js þ zi j ¼ js þ pi j
1 jKj
ð7:3-8Þ
i¼1
For K ¼ 1, relation (7.3-8) gives m Y
js þ zi j ¼ 0
ð7:3-9Þ
i¼1
Relation (7.3-9) is satisfied for s ¼ zi . Also, for K ! 1 and for m < n, relation (7.3-8) is satisfied for s ! 1. Therefore, the points of the root locus for K ! 1 are the zeros z1 ; z2 ; . . . ; zm of GðsÞFðsÞ and infinity when m < n. Theorem 7.3.3 (Number of Branches) The number of branches of the root locus is maxðm; nÞ, where m and n are the number of zeros and poles of GðsÞFðsÞ, respectively. Proof Introducing Eq. (7.3-4) in Eq. (7.3-1), we obtain m Y
1 þ GðsÞFðsÞ ¼ 1 þ K
ðs þ zi Þ
i¼1 n Y
¼0
ð7:3-10Þ
ðs þ pi Þ
i¼1
The closed-loop system characteristic polynomial pc ðsÞ is given by pc ðsÞ ¼
n Y i¼1
ðs þ pi Þ þ K
m Y ðs þ zi Þ
ð7:3-11Þ
i¼1
The degree of pc ðsÞ will be maxðm; nÞ. Therefore, the number of roots (and hence the number of branches of the root locus) is maxðm; nÞ. Theorem 7.3.4 (Symmetry About the Real Axis) The root locus for K 2 ð1; þ1Þ is symmetrical about the real axis. Proof Since all complex roots of the pc ðsÞ appear always in conjugates pairs, it follows that the root locus will be symmetrical about the real axis. Theorem 7.3.5 (Asymptotes) For large values of s, the root locus for K 0 approaches asymptotically the straight lines having the following angles
The Root Locus Method
¼
ð2 þ 1Þ ; nm
277
¼ 0; 1; . . . ; jn mj 1
ð7:3-12Þ
The root locus for K 0 approaches asymptotically the straight lines having the following angles: ¼
2 ; nm
¼ 0; 1; . . . ; jn mj 1
ð7:3-13Þ
Proof The characteristic equation (7.3-10) can be written as n Y
ðs þ pi Þ
i¼1 m Y
¼ K
ðs þ zi Þ
i¼1
Dividing the two polynomials, we obtain snm þ ðb1 d1 Þsnm1 þ ¼ K
ð7:3-14Þ
where use was made of relation (7.3-4). For large values of s, the left-hand side of relation (7.3-14) may be approximated by its first two terms, as follows: snm þ ðb1 d1 Þsnm1 ¼ K
ð7:3-15Þ
Relation (7.3-15) can be written as b1 d1 nm s 1þ ¼ K s Taking the ðn mÞ root of both parts of the above equation, we obtain b d1 1=ðnmÞ ¼ ðKÞ1=ðnmÞ or s 1þ 1 s b d1 þ ¼ ðKÞ1=ðnmÞ s 1þ 1 ðn mÞs
ð7:3-16Þ
where the expansion of ð1 þ aÞ1=ðnmÞ was used. For large values of s, this expansion is approximated by its first two terms, in which case Eq. (7.3-16) becomes sþ
b1 d1 ¼ ðKÞ1=ðnmÞ nm
ð7:3-17Þ
Since, for K 0, it holds that K ¼ jKje jð2 þ1Þ ;
¼ 0; 1; 2;
and for K 0, it holds that K ¼ jKje j2 ;
¼ 0; 1; 2; . . .
it follows that for K 0, we have ðKÞ1=ðnmÞ ¼ jKj1=ðnmÞ exp½ jðð2 þ 1Þ=ðn mÞÞ;
¼ 0; 1; 2; . . .
278
Chapter 7
and for K 0, we have ðKÞ1=ðnmÞ ¼ jKj1=ðnmÞ exp½ jð2 =ðn mÞÞ;
¼ 0; 1; 2; . . .
Therefore, relation (7.3-17) can be written as þ j! þ
b1 d1 ¼ jKj1=ðnmÞ exp½ jðð2 þ 1Þ=ðn mÞÞ; nm
for
K0 ð7:3-18aÞ
þ j! þ
b1 d1 ¼ jKj1=ðnmÞ exp½ jðð2 Þ=ðn mÞÞ; nm
for
K0 ð7:3-18bÞ
for ¼ 0; 1; 2 . . . ; where s ¼ þ j!. Relations (7.3-18a,b) hold if both sides of the two equations have the same phase. That is, if 2 3 6 tan1 4 and
2 6 tan1 4
! 7 ð2 þ 1Þ ; 5¼ b1 d1 nm þ nm
K0
for
3 ! 2 7 ; 5¼ b1 d1 nm þ nm
for
K0
Solving the above equations for !, we obtain 2 þ 1Þ b d1 ! ¼ tan þ 1 ; K0 nm nm 2 b1 d1 ! ¼ tan þ ; K0 nm nm
ð7:3-19aÞ ð7:3-19bÞ
Relations (7.3-19a,b) are actually straight lines in the s-plane with angles (slopes) as follows: ð2 þ 1Þ ; for K0 nm 2 ¼ ; for K0 nm
¼
for ¼ 0; 1; 2; . . . A careful examination of the values of the angles , as takes on the values 0; 1; 2; . . . ; shows that there are only 2jn mj asymptotes which correspond to the values of ¼ 0; 1; . . . ; jn mj 1. Theorem 7.3.6 (Intersection of Asymptotes) All the 2jn mj asymptotes of the root locus intersect on the real axis at the point 1 , where n X
b d1 ¼ 1 ¼ 1 nm
i¼1
pi
m X i¼1
nm
zi ð7:3-20Þ
The Root Locus Method
279
Proof From relations (7.3-19a,b) it immediately follows that all the asymptotes intersect the real axis at the point 1 specified in Eq. (7.3-20). Theorem 7.3.7 (Real Axis Segments) A segment of the real axis can be part of the root locus if the following hold: 1. 2.
For K 0, the number of real poles and zeros of GðsÞFðsÞ, which are to the right of the segment, is odd. For K 0, the number of real poles and zeros of GðsÞFðsÞ, which are to the right of the segment, is even.
Proof Assume that the point s1 is located on the real axis. According to relation (7.3-5b), the point s1 is a point of the root locus for K 0, if it satisfies the following equation: m X
s 1 þ zi
i¼1
n X
s1 þ pi ¼ ð2 þ 1Þ;
¼ 0; 1; 2; . . .
ð7:3-21Þ
i¼1
It is obvious that the complex conjugate poles and zeros do not contribute in Eq. (7.3-21) because their angles cancel each other. Similarly, with the real poles and zeros in Eq. (7.3-21), which are located to the left of the point s1 . On the other hand, the phase contribution of every real pole and zero which is located to the right of the point s1 , is . Let pi ; i ¼ 1; 2; . . . ; q and zi ; i ¼ 1; 2; . . . ; r to be the poles and zeros of GðsÞFsÞ which are located on the real axis and to the right of the point s1 . In this case, Eq. (7.3-21) yields r q ¼ ðr qÞ ¼ ð2 þ 1Þ
ð7:3-22Þ
Relation (7.3-22) holds if r q ¼ 2 þ 1, i.e., when either r or q is odd (or when r þ q is odd). Working likewise for K 0, where relation (7.3-21) has the form m X i¼1
s 1 þ zi
n X
s1 þ pi ¼ 2 ;
¼ 0; 1; 2; . . .
ð7:3-23Þ
i¼1
we conclude that relation (7.3-23) holds if r or q is even (or when r þ q is even). Theorem 7.3.8 (Breakaway Points) Assume that the characteristic equation 1 þ GðsÞFðsÞ ¼ 0
ð7:3-24Þ
has repeated roots. These roots are called breakaway points of the root locus and are also roots of the equation d ½GðsÞFðsÞ ¼ 0 ds
ð7:3-25Þ
280
Chapter 7
Proof Let s ¼ be a repeated root of multiplicity r. In this case, Eq. (7.3-24) can be factored out as follows: 1 þ GðsÞFðsÞ ¼ ðs Þr qðsÞ ¼ 0
ð7:3-26Þ
where qðsÞ is a rational function. If we take the derivative with respect to s of both sides of Eq. (7.3-26), we have d dqðsÞ ½GðsÞFðsÞ ¼ rðs Þr1 qðsÞ þ ðs Þr ds ds
ð7:3-27Þ
Since we have assumed that r 2, it follows that the right-hand side of Eq. (7.3-27) becomes zero for s ¼ . Thus, s ¼ is also a root of Eq. (7.3-25). Relation (7.3-25) is a necessary but not a sufficient condition. This means that the breakaway points are roots of Eq. (7.3-25), but all roots of Eq. (7.3-25) are not necessarily breakaway points. Therefore, in order to make sure that a certain root of Eq. (7.3-25) is a breakaway point of the root locus, it is sufficient that this root satisfies Eq. (7.3-24) for some real value of K. 7.3.3
Additional Information for Constructing the Root Locus
In addition to the above eight theorems, we also give the following useful information for the construction of a root locus. 1
Angles of Departure and Arrival of the Root Locus
The angle of the root locus at the poles and zeros of GðsÞFðsÞ can be calculated on the basis of Eq. (7.3-5b). As an example, consider calculating the angle at the arbitrary pole pq . To this end, assume that the point s1 is very close to the pole pq . Thus, if we solve Eq. (7.3-5b) for the angle at the pole pq , we obtain pq ¼
s1 þ pq ¼ ð2 þ 1Þ þ
m X
s 1 þ zi
n X
s 1 þ pi
ð7:3-28Þ
i¼1 i6¼q
i¼1
The results are similar for the angle of an arbitrary zero zp . The angles pq and zq are called the departure and arrival angles, respectively. Their graphical representation is given in Figure 7.4. Since the departure angle of the root locus for K 0 is equal to the arrival angle for K 0, as shown in Figure 7.4, the arrival angle at the point s1 ! zq can be calculated from the relation (7.35c) as follows zq ¼
s1 þ zq ¼ 2
m X i¼1 i6¼q
2
s 1 þ zi þ
n X
s 1 þ pi
ð7:3-29Þ
i¼1
Intersection of the Root Locus with the Imaginary Axis
The intersection of the root locus with the imaginary axis (if there exists such an intersection) is a set of points beyond which the system becomes unstable. A method for determining these points is based on the Routh’s criterion and is illustrated in the examples presented in the following subsection.
The Root Locus Method
Figure 7.4
7.3.4
281
Angles of departure and arrival of the root locus.
Root Locus of Practical Control Systems
In this subsection we illustrate the application of the derived theoretical results by constructing the root locus of several practical control systems. To facilitate the understanding of the general procedure for constructing the root locus, we start by applying this general procedure to the introductory Example 7.2.1 in the example that immediately follows. Example 7.3.1 Determine the root locus of the closed-loop system of Example 7.2.1 using the construction method presented in this section, i.e., the eight theorems of Subsec. 7.3.2 and the additional information of Subsec. 7.3.3. Solution Following the root locus construction method step by step, we have: 1. The root locus points for K ¼ 0 are the poles of GðsÞFðsÞ, e.g., the points s ¼ 0 and s ¼ 6. These are the root locus starting points for K 0. 2. The root lcous points for K ! 1 are the zeros of GðsÞFðsÞ. Since there are no zeros in GðsÞFðsÞ and since the degree of the numerator is smaller than that of the denominator ðm < nÞ, the root locus points for K ! 1 are at infinity. These are the ending points of the root locus. 3. The number of branches of the root locus is maxðm; nÞ ¼ maxð0; 2Þ ¼ 2. 4. The angles of the asymptotes are ¼
ð2 þ 1Þ ; ¼ 0; 1; 2
¼
2 ; ¼ 0; 1; 2
for
for
K0
K0
Therefore, the asymptotes are straight lines having the following slopes:
282
Chapter 7
; 2 0 ¼ 0;
0 ¼
3 ; 2 1 ¼ ; 1 ¼
for for
K0
and
K0
5. The point of intersection of the asymptotes is b1 d1 60 ¼ 3 ¼ 2 nm 6. The segments of the real axis that can be part of the root locus are
1 ¼
a. For K 0, the segment from 6 to 0. b. For K 0, the segment from 1 to 6 and from 0 to þ1. 7. The root locus breakaway points are roots of the following equation: d d K Kð2s þ 6Þ ½GðsÞFðsÞ ¼ ¼0 ¼ 2 ds ds sðs þ 6Þ s ðs þ 6Þ2 From the above equation, the point s ¼ 3 appears to be a candidate breakaway point. To be a breakaway point, it must satisfy the equation 1 þ GðsÞFðsÞ for any real value of K. Let s ¼ 3. Then 1 þ Gð3ÞFð3Þ ¼ 1
K ¼0 9
Hence, equation 1 þ Gð3ÞFð3Þ ¼ 0 is satisfied for K ¼ 9. Thus, the point s ¼ 3 is a breakaway point of the root locus. 8. The departure angles at the two poles of GðsÞFðsÞ are calculated according to the Eq. (7.3-28) as follows: a. At the pole s ¼ 0: as s1 ! 0, we have 0 ¼
s1 þ 0 ¼ ð2 þ 1Þ
s1 þ 6 ¼ ð2 þ 1Þ 0
Taking the smallest angle, e.g., for ¼ 0, we have 0 ¼ . b. At the pole s ¼ 6: as s1 ! 6, we have 6 ¼
s1 þ 6 ¼ ð2 þ 1Þ
s1 ¼ ð2 þ 1Þ
Taking the smallest angle, e.g., for ¼ 0, we have 6 ¼ 2 or 6 ¼ 0. 9. The root locus intersection with the imaginary axis is determined using Routh’s criterion. To this end, construct the Routh’s table of the characteristic polynomial pc ðsÞ ¼ sðs þ 6Þ þ K, as follows: K s2 1 0 s1 6 s0 K From the first column of the Routh table we observe that the system changes from stable to unstable (i.e., it intersects the imaginary axis) when K ¼ 0. Next, using the row s2 , we form the auxiliary polynomial AðsÞ ¼ s2 þ K. For K ¼ 0, the roots of AðsÞ are s ¼ j0. Thus, the point s ¼ j0 is the root locus intersection with the imaginary axis. Using the above results one can construct the root locus, which, as expected, will be exactly the same as that of Figure 7.3.
The Root Locus Method
283
Example 7.3.2 This example refers to the control of the nuclear reactor which was presented in Sec. 1.4 (see Figure 1.16). The reactor’s block diagram is given in Figure 7.5, where the reactor is described by the simplified model of a first-order system. We assume that the regulator Gc ðsÞ has two terms, an analog and an integral, so that Gc ðsÞ ¼ K1 þ K2 =s. Therefore, 1 Kðs þ Þ ¼ GðsÞ ¼ Gc ðsÞGr ðsÞ ¼ ðK1 þ K2 =sÞ Ts þ 1 sðTs þ 1Þ where K ¼ K1 and ¼ K2 =K1 . To simplify, let ¼ 2 and T ¼ 1, in which case the open-loop transfer function becomes GðsÞFðsÞ ¼
Kðs þ 2Þ sðs þ 1Þ
Determine the root locus of the closed-loop system using the construction method presented in this section. Solution We have: 1. The root locus points for K ¼ 0 of GðsÞFðsÞ are s ¼ 0 and s ¼ 1. These points are the starting points of the root locus. 2. The root locus points for K ! þ1 are s ¼ 2 and infinity. Those points are the ending points of the root locus. 3. The number of branches of the root locus is maxðm; nÞ ¼ maxð1; 2Þ ¼ 2. 4. The angles of the asymptotes are ¼ ð2 þ 1Þ; ¼ 2 ;
¼ 0;
¼ 0;
when K 0
when K 0
Therefore, the asymptotes are straight lines having the following slopes 0 ¼ ;
when
K0
and
0 ¼ 0;
when
5. The point of intersection of the asymptotes is 1 ¼
Figure 7.5
b1 d1 ¼ ð1 2Þ ¼ 1 nm
Simplified block diagram of a nuclear reactor control system.
K0
284
Chapter 7
It is noted that because jn mj 1 ¼ 0, we have only one asymptote, which is the real axis. Therefore, there is no point of intersection of the asymptotes, and hence the point 1 ¼ 1 does not have any particular meaning. 6. The segments of the real axis that can be part of the root locus are a. For K > 0, the segment from 1 to 2 and the part from 1 to 0. b. For K < 0, the segment from 2 to 1 and from 0 to þ1. 7. The root locus breakaway points are roots of the following equation: d d Kðs þ 2Þ sðs þ 1Þ ðs þ 2Þð2s þ 1Þ ½GðsÞFðsÞ ¼ ¼K ¼0 ds ds sðs þ 1Þ s2 ðs þ 1Þ2 Simplifying the above equation yields s2 þ 4s þ 2 ¼ 0
pffiffiffi The rootspof ffiffiffi the above algebraic equation are s1 ¼ 2 þ 2 ¼ 0:586 and s2 ¼ 2 2 ¼ 3:414. For s1 and s2 to be breakaway points, they must satisfy the equation 1 þ GðsÞFðsÞ ¼ 0 for any real value of K. For the root s1 we have 1 þ Gð0:586ÞFð0:586Þ ¼ 1 þ K
1:414 ¼0 ð0:586Þð0:414Þ
The above equation is satisfied for K ¼ 0:1716. For the root s2 we have 1 þ Gð3:414ÞFð3:414Þ ¼ 1 þ K
ð1:414Þ ¼0 ð3:414Þð2:414Þ
The above equation is satisfied for K ¼ 5:8274. Therefore, both points s1 ¼ 0:586 and s2 ¼ 3:414 are breakaway points of the root locus. 8. The root locus departure angles are calculated using Eq. (7.3-28), as follows: a. At the pole s ¼ 0: as s1 ! 0 we have 0 ¼
s1 ¼ ð2 þ 1Þ þ
s1 þ 2
s1 þ 1 ¼ 0 þ 0 ¼ or
for ¼ 0. b. At the pole s ¼ 1: as s1 ! 1 we have 1 ¼
s1 þ 1 ¼ ð2 þ 1Þ þ
s1 þ 2
s1 ¼ þ 0 ¼ 2 or 0
for ¼ 0. The root locus arrival angles are calculated using Eq. (7.3-29) as follows: here, we have only one zero, namely, the zero s ¼ 2. At the zero s ¼ 2, as s1 ! 2, we have 2 ¼
s1 þ 2 ¼ 2 þ
s1 þ
s1 þ 1 ¼ 0 þ þ ¼ 2 or 0
for ¼ 0. 9. The root locus intersection with the imaginary axis is determined using the Routh’s criterion. To this end, construct the Routh table of the characteristic polynomial pc ðsÞ ¼ sðs þ 1Þ þ Kðs þ 2Þ ¼ s2 þ ðK þ 1Þs þ 2K, as follows:
The Root Locus Method
s2 s1 s0
1 K þ1 2K
285
2K 0
The system is stable if the inequalities K > 1 and K > 0 hold simultaneously. Thus, for K > 0 the system is stable. Next, using the row s2 , we form the auxiliary polynomial AðsÞ ¼ s2 þ 2K. For K ¼ 0 the auxiliary polynomial AðsÞ ¼ s2 þ 2K gives s ¼ j0. Therefore, the root locus is intersecting the j!-axis at the point s ¼ j0. Using the above results one can construct the root locus for K > 0, as shown in Figure 7.6. Example 7.3.3 This example refers to the automatic piloting system for supersonic airplanes (Figure 7.7a), which assists the aerodynamic stability of the plane, thus making the flight more stable and more comfortable. A simplified block diagram of this system is given in Figure 7.7b. The aircraft dynamics are approximated by a second-order system, where K is a parameter which changes according to the flight conditions (e.g., fast landing or take-off, steady flight, etc). Assume that there are no disturbances, i.e., DðsÞ ¼ 0. Determine the closed-loop system root locus for K > 0 and the range of values of K such that the closed-loop system is stable. Solution From the block diagram of Figure 7.7b we have GðsÞFðsÞ ¼
Kðs þ 4Þ sðs þ 6Þðs þ 8Þðs2 þ 2s þ 2Þ
Following the root locus construction method step by step, we have:
Figure 7.6
The root locus of the nuclear reactor closed-loop system.
286
Chapter 7
Figure 7.7
Automatic piloting system for supersonic airplanes. (a) Supersonic airplane; (b) simplified block diagram.
1. The root locus points for K ¼ 0 are s ¼ 0; s ¼ 6; s ¼ 8; s ¼ 1 þ j, and s ¼ 1 j. These points are the root locus starting points. 2. The root locus points for K ! þ1 are s ¼ 4 and infinity. These points are the root locus ending points. 3. The number of branches of the root locus is maxðm; nÞ ¼ maxð1; 5Þ ¼ 5. 4. The angles of the asymptotes are ¼
ð2 þ 1Þ ð2 þ 1Þ ¼ ; nm 4
¼ 0; 1; 2; 3;
when
K >0
Therefore, the asymptotes are straight lines having the following slopes 0 ¼
; 4
3 1 ¼ ; 4
5 2 ¼ ; 4
and
7 3 ¼ 4
5. The point of intersection of the asymptotes is 1 ¼
b1 d1 16 4 ¼ 3 ¼ 4 nm
6. The segments of the real axis that can be part of the root locus for K > 0 are the segments from 0 to 4 and from 6 to 8.
The Root Locus Method
287
7. The root locus breakaway points are roots of the equation d d Kðs þ 4Þ ðsÞ ½GðsÞFðsÞ ¼ ¼K ds ds sðs þ 6Þðs þ 8Þðs2 þ 2s þ 2Þ ðsÞ where ðsÞ ¼ sðs þ 6Þðs þ 8Þðs2 þ 2s þ 2Þ ðs þ 4Þ½2sðs þ 7Þðs2 þ 2s þ 2Þ þ ðs þ 6Þðs þ 8Þð3s2 þ 4s þ 2Þ Because the determination of the roots of the equation ðsÞ ¼ 0 is quite difficult, an attempt will be made to come to a conclusion regarding the approximate position of the root locus breakaway points, by circumventing the direct calculation of the roots of the equation ðsÞ ¼ 0. To this end, taking advantage of the information that we already have about the root locus, it appears that there is only one breakaway point which lies between the points ð6; 0Þ and ð8; 0Þ. Indeed, since the points ð6; 0Þ and 8; 0Þ are starting points, the root locus which begins from these two points must intersect between the points ð6; 0Þ and ð8; 0Þ and then change course, moving away from the real axis. 8. The root locus departure angles are calculated according to Eq. (7.3-28) as follows: a. At the pole s ¼ 0: as s1 ! 0 for ¼ 0, we have 0 ¼
s1 ¼ ð2 þ 1Þ þ
s1 þ 1 j
¼ þ 0 0 0
sþ4
s1 þ 6
s1 þ 8
s1 þ 1 þ j
¼ 4 4
b. At the pole s ¼ 6: as s1 ! 6 and for ¼ 0, we have 6 ¼
s1 þ 6 ¼ ð2 þ 1Þ þ
s1 þ 4
s1
s1 þ 8
s1 þ 1 þ j
s1 þ 1 j
¼ þ ðÞ ðÞ 0 ðÞ ðÞ ¼ where ¼
s1 þ 1 þ j.
c. At the pole s ¼ 8: as s1 ! 8 and for ¼ 0, we have 8 ¼
s1 þ 8 ¼ ð2 þ 1Þ þ
s1 þ 4
s1
s1 þ 6
s1 þ 1 j
¼ þ ðÞ ðÞ ðÞ ðÞ ðÞ ¼ 0 d. At the pole s ¼ 1 j: as s1 ! 1 j and for ¼ 0, we have
s1 þ 1 þ j
288
Chapter 7
1j ¼
s1 þ 1 þ j ¼ ð2 þ 1Þþ s1 þ 4
¼ þ
s1
3j
s1 þ 6 1j
s1 þ 8 5j
s1 þ 1 j 7j
2j
¼ 1808 þ ð18:438Þ ð1358Þ ð11:308Þ ð8:138Þ ð908Þ ¼ 468 e. At the pole s ¼ 1 þ j: from the root locus symmetry about the real axis we conclude that 1þj ¼ 1j ¼ 468 The arrival angle at the zero s ¼ 4 is calculated according to Eq. (7.3-29) as follows: as s1 ! 4 and for ¼ 0, we have 4 ¼
s1 þ 4 ¼ 2 þ s1 þ
s1 þ 6 þ
s1 þ 8 þ
s1 þ 1 þ j þ
s1 þ 1 j
¼0þþ0þ0þ¼ 9. The root locus intersection with the imaginary axis is determined as follows: construct the Routh table of the characteristic polynomial pc ðsÞ ¼ sðs þ 6Þðs þ 8Þðs2 þ 2s þ 2Þ þ Kðs þ 4Þ ¼ s5 þ 16s4 þ 78s3 þ 124s2 þ ð96 þ KÞs þ 4K as follows: s5 s4
1 16
78 124
96 þ K 4K
0 0
s3
70:25
96 þ 0:75K
0
0
2
102:1352 0:1708K 9805 220:7972K 0:1281K 2 102:1352 0:1708K 4K
4K
0
0
0
0
0
0
0
0
s
s1 s0
From the Routh criterion it is well known that the closed-loop system is stable if all the elements of the first column of the Routh table have the same sign. For this to hold, the inequalities 102:1352 0:1708K > 0 (or K < 597:89), 9805 220:7972K 0:1281K 2 > 0 (1767 < K < 43:3) and 4K > 0, must be satisfied simultaneously. From these three inequalities it immediately follows that the system is stable when 0 < K < 43:3. Clearly, for the values of K ¼ 0 and K ¼ 43:3 the root locus intersects the j!-axis. The points of intersection are calculated from the auxiliary equation AðsÞ ¼ ð102:1352 0:1708KÞs2 þ 4K ¼ 0, which is formed using the row s2 . For K ¼ 43:3 the auxiliary equation becomes 94:739s2 þ 173:2 ¼ 0 which gives the points of intersection s ¼ j1:352. For K ¼ 0 the auxiliary equation becomes 102:1352s2 ¼ 0, which gives the point of intersection s ¼ 0. Using all the above information we construct the root locus for K > 0 as shown in Figure 7.8.
The Root Locus Method
Figure 7.8
289
The root locus of the supersonic airplane closed-loop system.
Example 7.3.4 Consider the closed-loop control system which controls the thickness of metal sheets, shown in Figure 1.10 of Chap. 1. The system is approximately described as in Figure 7.9. Determine the root locus for the following two cases: (a) Gc ðsÞ ¼ K (b) Gc ðsÞ ¼ Kðs þ 0:5Þ Solution Case (a) The open-loop transfer function is
Figure 7.9
Simplified block diagram of the thickness control system.
290
Chapter 7
Gc ðsÞGðsÞ ¼
K s ðs þ 1Þ 2
Following the root locus construction method step by step, we have: 1. The points of the root locus for K ¼ 0 are s ¼ 0 and s ¼ 1. These points are the root locus starting points for K 0. 2. The points of the root locus for K ! 1 are the root locus ending points, which are at infinity. 3. The number of branches of the root locus is maxðm; nÞ ¼ maxð0; 3Þ ¼ 3. 4. The angles of the asymptotes are ð2 þ 1Þ ;
¼ 0; 1; 2; 3 2 ¼ ;
¼ 0; 1; 2; for 3
¼
K0
for
K0
Therefore, the asymptotes are straight lines having the following slopes: 0 ¼
; 3
0 ¼ 0;
1 ¼ ; 1 ¼
5 ; 3 4 2 ¼ ; 3
2 ¼
2 ; 3
when when
K0 K0
5. The point of intersection of the asymptotes is n X
1 ¼
i¼1
pi
m X i¼1
nm
zi ¼
10 1 ¼ 3 3
6. The segments of the real axis that can be part of the root locus are a. For K 0, the segment from 1 to 1. b. For K 0, the segment from 1 to 0 and the segment from 0 to þ1. 7. The root locus breakaway points are roots of the equation " # d 2sðs þ 1Þ þ s2 ½G ðsÞGðsÞ ¼ K ¼0 ds c s4 ðs þ 1Þ2 From the above equation we conclude that candidate breakaway points of the root locus are s ¼ 0 and s ¼ 2=3. For these points to be breakaway points, they must satisfy the equation 1 þ Gc ðsÞGðsÞ ¼ 0 for any real value of K. The point s ¼ 0 satisfies the above equation for K ¼ 0 and the point s ¼ 2=3 for K ¼ 4=27. Hence, theya re both breakaway points. 8. The root locus departure angles are a. At the double pole s ¼ 0: as s1 ! 0 and " ! 0, we have 0 ¼
s1 þ 0 j" ¼ ð2 þ 1Þ ¼ ð2 þ 1Þ 2
s1 þ 1
Choosing the smallest angles, e.g., for ¼ 0, we obtain
s1 þ 0 j"
The Root Locus Method
0 ¼
3 2
and
291
0 ¼
2
b. At the pole s ¼ 1: as s1 ! 1, we have 1 ¼
s1 þ 1 ¼ ð2 þ 1Þ 2
s1 ¼ ð2 þ 1Þ 2
Choosing the smallest angles, e.g., for ¼ 0, we have 1 ¼ 3. The results presented above are adequate to construct the root locus sought, as shown in Figure 7.10a.
Figure 7.10 The root locus of the closed-loop system of Example 7.4.3: (a) when GC ðsÞ ¼ K; (b) when GC ðsÞ ¼ Kðs þ 0:5Þ.
292
Chapter 7
Case (b) The open-loop transfer function has the form Gc ðsÞGðsÞ ¼
Kðs þ 0:5Þ s2 ðs þ 1Þ
Following the root locus construction method step by step, we have: 1. The root locus starting points are at s ¼ 0 and s ¼ 1 for K 0. 2. The root locus ending points are at s ¼ 0:5 and at infinity for K 0. 3. The number of branches of the root locus is maxð1; 3Þ ¼ 3. 4. The angles of the asymptotes are a. For K 0, 0 ¼ =2, and 1 ¼ 3=2 b. For K 0, 0 ¼ 0, and 1 ¼ . 5. The point of intersection of the asymptotes is 1 ¼
0:5 ¼ 0:25 2
6. The segments of the real axis that can be part of the root locus are a. For K 0, the segment ð1; 0:5Þ b. For K 0, the segments ð1; 1Þ, ð0:5; 0Þ, and ð0; þ1Þ. 7. The root locus breakaway points are roots of the equation d ½G ðsÞGðsÞ ¼ 0 ds c
or
sð2s2 þ 2:5s þ 1Þ ¼ 0
We have three roots: s ¼ 0 and s ¼ 0:625 j0:33. From these three roots, only the root s ¼ 0 satisfies the equation 1 þ Gc ðsÞGðsÞ ¼ 0 for K ¼ 0. For the other two roots, there are no real values of K which satisfy the equation 1 þ Gc ðsÞGðsÞ ¼ 0. 8. The root locus departure angles are a. At the pole s ¼ 0: as s1 ! 0 and " ! 0, we have 0 ¼
s1 þ 0 j" ¼ 2ð þ 1Þ þ ¼ ð2 þ 1Þ þ 0 2
s1 þ 0:5
s1 þ 0 j"
Choosing the smallest angles, i.e., for ¼ 0, we have 0 ¼
3 2
and
0 ¼
2
b. At the pole s ¼ 1: as s1 ! 1, we have 1 ¼
s1 þ 1 ¼ ð2 þ 1Þ þ
s1 þ 0:5 2
s1 þ 0 ¼ ð2 þ 1Þ þ 2
Choosing the smallest angles, i.e., for ¼ 0, we have 1 ¼ 2
or
0
9. The root locus arrival angle is at the zero ¼ 0:5: as s1 ! 0:5, we have 0:5 ¼ or
s1 þ 0:5 ¼ 2 þ 2
s1 þ 0 þ
s1 þ 1 ¼ 2 þ 2 þ 0
The Root Locus Method
293
0:5 ¼ 2 ¼ 0 Using the above results, one may construct the root locus, as shown in Figure 7.10b. In Fig. 7.10a, where Gc ðsÞ ¼ K, the closed-loop system is unstable. In Fig. 7.10b, where Gc ðsÞ ¼ Kðs þ 0:5Þ, the closed-loop system is stable for K > 0. This is obviously because we added the zero s ¼ 0:5 in the loop transfer function. It is noted that this zero lies between the poles 1 and 0. The effects of adding poles and/ or zeros in the loop transfer function is studied in Sec.7.5. The following example demonstrates that in several cases it is possible to find an analytical expression for a certain segment of the root locus of a system (another interesting similar problem is stated in Problem 9 of Sec. 7.6). Example 7.3.5 Consider the closed-loop system of Figure 7.11. Show that the root locus segment which is not located on the real axis is described analytically in polar coordinates as follows: 3 cos ¼ 3, where s ¼ e j . Solution The closed-loop system transfer function is GðsÞ ¼ HðsÞ ¼ 1 þ GðsÞ
Ks Ks ðs þ 1Þðs þ 2Þðs 3Þ ¼ 3 Ks s þ ðK 7Þs 6 1þ ðs þ 1Þðs þ 2Þðs 3Þ
The characteristic polynomial pc ðsÞ of the closed-loop system transfer function is pc ðsÞ ¼ s3 þ ðK 7Þs 6. Replacing s with e j in pc ðsÞ, we obtain that the points of the root locus which are not located on the real axis satisfy the equation ð e j Þ3 þ ðK 7Þ e j 6 ¼ 0
or
3 e3j þ ðK 7Þ e j 6 ¼ 0
or ½ 3 cos 3 þ ðK 7Þ cos 6 þ j½ 3 sin 3 þ ðK 7Þ sin ¼ 0 The points of the root locus must satisfy both the real and the imaginary part in the above equation, i.e., there must be
3 cos 3 þ ðK 7Þ cos 6 ¼ 0
and
3 sin 3 þ ðK 7Þ sin ¼ 0
Solving the second equation for ðK 7Þ and introducing the result into the first we obtain
Figure 7.11
The closed-loop system of Example 7.3.5.
294
Chapter 7
3 cos 3
3 cos sin 3 6¼0 sin
Consider the trigonometry relations: cos 3 ¼ cos3 3 sin2 cos and sin 3 ¼ 3 sin cos2 sin3 . Using these two relations, the above equation becomes
3 ½cos3 þ sin2 cos ¼ 3 or
or
3 cos ðcos2 þ sin2 Þ ¼ 3
3 cos ¼ 3
Remark 7.3.1 From the examples presented in the present subsection it must be obvious that the form of the root locus changes drastically with the position of the poles and zeros of GðsÞFðsÞ. This is demonstrated in Figure 7.12, which gives various pole-zero configurations of GðsÞFðsÞ and their corresponding root loci. 7.4
APPLYING THE ROOT LOCUS METHOD FOR DETERMINING THE ROOTS OF A POLYNOMIAL
The root locus method can be used to determine the roots of a polynomial. This idea can be easily illustrated by the following two simple examples. Example 7.4.1 Consider the polynomial pðsÞ ¼ s2 þ 2s þ 2
ð7:4-1Þ
Determine the roots of pðsÞ using the root locus method. Solution To determine the roots of pðsÞ using the root locus method, we find an appropriate form of 1 þ GðsÞFðsÞ such that the characteristic polynomial of 1 þ GðsÞFðsÞ is equal to pðsÞ. Such a form is 1 þ GðsÞFðsÞ ¼ 1 þ
Kðs þ 2Þ sðs þ 1Þ
ð7:4-2Þ
where, for K ¼ 1, the characteristic polynomial of Eq. (7.4-2) is equal to pðsÞ. Therefore, the roots of pðsÞ are the points of the root locus of Eq. (7.4-2) when K ¼ 1. Since the root locus of Eq. (7.4-2) is the root locus of the Example 7.3.2, the roots of pðsÞ can be found from Figure 7.6 to be 1 þ j and 1 j. Example 7.4.2 Consider the polynomial pðsÞ ¼ s3 þ 3s2 þ 2s þ 6 Determine the roots of pðsÞ using the root locus method.
ð7:4-3Þ
The Root Locus Method
Figure 7.12
295
Various pole-zero configurations of GðsÞFðsÞ and the corresponding root loci
for K > 0.
Solution Repeating the method used in the previous example, we obtain 1 þ GðsÞFðsÞ ¼ 1 þ
K sðs þ 1Þðs þ 2Þ
ð7:4-4Þ
The characteristic polynomial of Eq. (7.4-4), for K ¼ 6, is equal to pðsÞ. Therefore, the roots of pðsÞ are the points of the root locus of Eq. (7.4-4) when K ¼ 6. The root locusp offfiffiffiEq. (7.4-4) pffiffiis ffi given in Figure 7.13, where for K ¼ 6 we find that the roots are 3, j 2, and j 2. This can also be found from Eq. (7.4-3) if we expand pðsÞ as follows: pðsÞ ¼ sðs2 þ 2Þ þ 3ðs2 þ 2Þ ¼ ðs2 þ 2Þðs þ 3Þ.
296
Chapter 7
Figure 7.13
7.5
The root locus of Example 7.4.2.
EFFECTS OF ADDITION OF POLES AND ZEROS ON THE ROOT LOCUS
The root locus method is usually used to obtain an overall simple picture of the effect that the gain constant K has on the positions of the poles of a closed-loop system. It is also used to obtain an overall picture of the effect that has on the root locus the addition of poles and/or zeros in the loop transfer function GðsÞFðsÞ. The addition of new poles and/or zeros in GðsÞFðsÞ is done as in Figure 7.14b and it aims at the improvement of the closed-loop system behavior. For the closed-loop system 7.14b the characteristic equation takes on the form 1 þ ½G1 ðsÞF1 ðsÞGðsÞFðsÞ ¼ 0
ð7:5-1Þ
The transfer functions G1 ðsÞ and F1 ðsÞ are the additional controllers introduced in the closed-loop system. Depending on the particular form of G1 ðsÞF1 ðsÞ the root locus of Eq. (7.5-1) may change drastically. In the next two subsections, we study this change that the original root locus undergoes when the additional controller G1 ðsÞF1 ðsÞ is included in the closed-loop system. 7.5.1
Addition of Poles and Its Effect on the Root Locus
Assume that G1 ðsÞF1 ðsÞ ¼
1 ðs þ 1 Þðs þ 2 Þ ðs þ p Þ
ð7:5-2Þ
The Root Locus Method
297
Figure 7.14 Closed-loop system without and with additional controllers. (a) Original closed-loop system; (b) closed-loop system with additional controllers.
Then, Eq. (7.5-1) becomes 1þ
GðsÞFðsÞ ¼0 ðs þ 1 Þ ðs þ p Þ
ð7:5-3Þ
Here, the root locus of Eqs (7.3-1) and (7.5-3) differ from each other in that the root locus of Eq. (7.5-3) is ‘‘moved’’ or ‘‘bended’’ more to the right of the root locus of Eq. (7.3-1). Thus, the addition of poles to closed-loop systems results in more unstable closed-loop systems. This fact will be illustrated by the following two examples. Example 7.5.1 Let GðsÞFðsÞ ¼
K ; sðs þ aÞ
a>0
ð7:5-4Þ
and G1 ðsÞF1 ðsÞ ¼
1 ; s þ 1
1 > a
Then, Eq. (7.5-3) becomes 1þ
K ¼0 sðs þ aÞðs þ 1 Þ
Study the stability of Eq. (7.5-5).
ð7:5-5Þ
298
Chapter 7
Figure 7.15
The effect of adding a pole to the root locus of Eq. (7.5-4).
Solution The root locus of Eqs (7.5-4) and (7.5-5) are given in Figure 7.15a and 7.15b, respectively. Figure 7.15b shows that the addition of one pole results in ‘‘bending’’ the root locus of Figure 7.15a more to the right. To be more precise, even though the entire root locus of Eq. (7.5-4) is located in the left-half complex plane for K 0, the root locus of Eq. (7.5-7) is partly located in the right-half complex plane. This means that while system (7.5-4) is stable for K 0, system (7.5-5) is unstable for large values of K and in particular for K > K1 . Example 7.5.2 Consider the open-loop transfer function (7.5-4). Also, consider the additional controller G1 ðsÞF1 ðsÞ, having the form G1 ðsÞF1 ðsÞ ¼
1 ; ðs þ 1 Þðs þ 2 Þ
2 > 1 > a
ð7:5-6Þ
Then, Eq. (7.5-3) becomes 1þ
K ¼0 sðs þ aÞðs þ 1 Þðs þ 2 Þ
ð7:5-7Þ
Study the stability of Eq. (7.5-7). Solution The root locus of Eq. (7.5-7) is given in Figure 7.16. It is clear that the addition of two poles ‘‘bends’’ the root locus of Eq. (7.5-4) even more to the right, a fact which makes the closed-loop system ‘‘more’’ unstable, compared with the case of adding only one pole. Indeed, if one compares Figure 7.15a (case of adding one pole) with
The Root Locus Method
Figure 7.16
299
The effect of adding two poles in the root locus of Eq. (7.5-4).
Figure 7.16 (case of adding two poles), one observes that the root locus in Figure 7.15a crosses the j!-axis when K ¼ K1 , while in Figure 7.16 it crosses the j!-axis when K ¼ K2 . Since K2 < K1 , it follows that the closed-loop system of Figure 7.16 becomes unstable for smaller values of K compared with Figure 7.15a. Hence, the system with characteristic equation (7.5-7) is more unstable compared with the system with characteristic equation (7.5-5).
7.5.2
Addition of Zeros and Its Effect on the Root Locus
Assume that G1 ðsÞF1 ðsÞ ¼ ðs þ 1 Þ. Then, Eq. (7.5-1) becomes 1 þ ðs þ 1 ÞGðsÞFðsÞ ¼ 0
ð7:5-8Þ
Here, the root locus of Eqs (7.3-1) and (7.5-8) differ from each other in that the root locus of Eq. (7.5-8) is ‘‘moved’’ or ‘‘bended’’ more to the left of the root locus of Eq. (7.3-1). Thus, the addition of zeros to closed-loop systems results in more stable closed-loop systems. This fact is illustrated by the following example. Example 7.5.3 Consider the loop transfer function Eq. (7.5-4). Then for G1 ðsÞF1 ðsÞ ¼ s þ 1 , Eq. (7.5-8) becomes
s þ 1 1þK ¼ 0; sðs þ aÞ
1 > a
Study the stability of Eq. (7.5-9).
ð7:5 9Þ
300
Chapter 7
Figure 7.17
The effect of adding a zero in the root locus of Eq. (7.5-4).
Solution The root locus of Eq. (7.5-9) is given in Figure 7.17, from which we conclude that the root locus of Eq. (7.5-9) is ‘‘bended’’ to the left of the root locus of Eq. (7.5-4) and therefore Eq. (7.5-9) is ‘‘more’’ stable than Eq. (7.5-4). PROBLEMS 1. Draw the root locus for the closed-loop systems having the following loop transfer functions: ðaÞ
K s2
ðbÞ
Kðs2 þ 4s þ 8Þ s2 ðs þ 4Þ
ðcÞ
Kðs þ 2Þ sðs þ 1Þðs þ 19Þ
ðdÞ
Kðs þ 2Þðs þ 6Þ sðs þ 4Þðs þ 3Þ
ðeÞ
Kðs þ 1Þ sðs þ 2Þðs þ 3Þðs þ 4Þ
ðfÞ
K ðs þ 1Þðs þ 2Þðs þ 3Þ
ðgÞ
Kðs þ 3Þ ðs þ 1Þðs þ 2Þðs þ 4Þ
ðhÞ
K ðs þ 1Þðs þ 2Þðs þ 3Þðs þ 4Þ
ðiÞ
K sðs þ 6s þ 25Þ
ðjÞ
Kðs þ 1Þ sðs2 þ 4s þ 8Þ
ðkÞ
Kðs þ 3Þ ðs þ 1Þðs þ 2Þðs2 þ 4s þ 8Þ
ðlÞ
Kðs þ 3Þðs þ 5Þ sðs þ 1Þðs þ 4Þðs2 þ 4s þ 8Þ
2
2. Figure 7.18 shows the block diagram of the direction control system of an automobile where the controller is a human driver. Draw the root locus of the system. 3. The block diagram of a position control system using a robot is shown in Figure 7.19. Draw the root locus of the system.
The Root Locus Method
301
Figure 7.18
4. The block diagram of a speed control system for an aircraft is shown in Figure 7.20. Draw the root locus of the system. 5. Consider the control system of a tape drive shown in Figure 7.21. Determine the root locus of the system for the following two cases: (a) (b)
K sþ4 Kðs þ 5Þ Gc ðsÞ ¼ sþ4
Gc ðsÞ ¼
6. Determine the root locus of the system shown in Figure 7.22, where Kðs þ 2Þ2 1 and GðsÞ ¼ sðs 1Þ s 7. Determine the root locus of the system in Figure 7.23 for the following three cases: Gc ðsÞ ¼
(a) Gc ðsÞ ¼ K (b) Gc ðsÞ ¼ Kðs þ 2Þ Kðs2 þ 2s þ 2Þ (c) Gc ðsÞ ¼ s 8. Draw the root locus of a submarine depth control system shown in Figure 6.3 (Example 6.4.10). 9. Consider the root locus of Example 7.3.2 given in Figure 7.6. Find an analytical expression of the root locus segment which has the shape of a perfect circle.
Figure 7.19
302
Figure 7.20
Figure 7.21
Figure 7.22
Figure 7.23
Chapter 7
The Root Locus Method
303
10. Find the roots of the following polynomials using the root locus method: (a) s3 þ 4s2 þ 4s þ 10 (b) s3 þ s2 þ 10s þ 10 11. Study the effect of variation of the parameter a on the root locus of the loop transfer functions: K sðs þ aÞ Kðs þ 1Þ (d) sðs þ 2Þðs þ aÞ
Kðs þ aÞ K (c) sðs þ 1Þ sðs þ 1Þðs þ aÞ Kðs þ 1Þ Kðs þ 1Þ (f) 2 2 ðs þ aÞðs þ 2Þðs þ 2s þ 2Þ s ðs þ aÞ (b)
(a)
(e)
12. Consider the speed control system described in Subsec. 3.13.3. Let Kg ¼ 1, Km ¼ 1, N ¼ 10, Lf ¼ 1, La ¼ 1, Ra ¼ 1, Jm ¼ 1, and B m ¼ 3. Then, GðsÞ ¼ Ka
10 ðs þ Rf Þ½ðs þ 1Þðs þ 3Þ þ Kb
For each of the four cases shown in Table 7.1 draw the root locus for Ka 0, and compare the results. 13. Consider the position control system described in Subsec. 3.12.2. For simplicity, let GðsÞ of Eq. (3.13-9) have the form 10 GðsÞ ¼ Ka sðs þ 1Þðs þ 3Þ þ Kb s For Kb ¼ 0:1, 1, and 10, the transfer function GðsÞ takes on the forms given in Table 7.2. Draw the root locus for the three cases, for Ka 0, and compare the results. Table 7.1 Rf ¼ 1:5
Rf ¼ 4
10 ðs þ 1:5Þ½ðs þ 1Þðs þ 3Þ þ 1 10 ¼ Ka ðs þ 1:5Þðs þ 2Þ2
Kb ¼ 1
GðsÞ ¼ G1 ðsÞ ¼ Ka
Kb ¼ 10
GðsÞ ¼ G2 ðsÞ ¼ Ka
Kb ¼ 1
GðsÞ ¼ G3 ðsÞ ¼ Ka
Kb ¼ 10
GðsÞ ¼ G4 ðsÞ ¼ Ka
10 ðs þ 1:5Þ½ðs þ 1Þðs þ 3Þ þ 10 10 ¼ Ka ðs þ 1:5Þðs2 þ 4s þ 13Þ 10 ðs þ 4Þ½ðs þ 1Þðs þ 3Þ þ 1 10 ¼ Ka ðs þ 4Þðs þ 2Þ2
10 ðs þ 4Þ½ðs þ 1Þðs þ 3Þ þ 10 10 ¼ Ka ðs þ 4Þðs2 þ 4s þ 13Þ
304
Chapter 7
Table 7.2 Kb ¼ 0:1
GðsÞ ¼ G1 ðsÞ ¼ Ka
10 10 ¼ Ka sðs þ 1Þðs þ 3Þ þ 0:1s sðs þ 1:05Þðs þ 2:95Þ
Kb ¼ 1
GðsÞ ¼ G2 ðsÞ ¼ Ka
10 10 ¼ Ka sðs þ 1Þðs þ 3Þ þ s sðs þ 2Þ2
Kb ¼ 10
GðsÞ ¼ G3 ðsÞ ¼ Ka
10 10 ¼ Ka sðs þ 1Þðs þ 3Þ þ 10s s½ðs2 þ 4s þ 4Þ þ 32
BIBLIOGRAPHY Books 1. 2. 3. 4. 5. 6. 7.
JJ D’Azzo, and CH Houpis. Linear Control System Analysis and Design, Conventional and Modern. New York: McGraw-Hill, 1975. JJ DiStefano III, AR Stubberud, IJ Williams. Feedback and Control Systems. Schaum’s Outline Series. New York: McGraw-Hill, 1967. RC Dorf, RE Bishop. Modern Control Analysis. London: Addison-Wesley, 1995. WR Evans. Control System Dynamics. New York: McGraw-Hill, 1954. GF Franklin, JD Powell, A Emami-Naeini. Feedback Control of Dynamic Systems. Reading, Massachusetts: Addison-Wesley, 1986. NS Nise. Control Systems Engineering. New York: Benjamin and Cummings, 1995. K Ogata. Modern Control Systems. London: Prentice Hall, 1997.
Articles 8. 9.
WR Evans. Control system synthesis by root locus method. AIEE Trans, Part II, 69:66– 69, 1950. WR Evans. Graphical analysis of control systems. AIEE Trans, Part II, 67:547–551, 1948.
8 Frequency Domain Analysis
8.1
INTRODUCTION
This chapter refers to the behavior of linear time-invariant systems in the frequency domain. Here the important control analysis and design tools of Nyquist, Bode, and Nichols diagrams are presented. It is important to stress that the control engineer should be able to understand the behavior of a system both in the time and in the frequency domain. Generally speaking, it is easier to understand the time domain behavior of a system compared with its frequency domain behavior. However (particularly with respect to the classical control methods), the time domain has the disadvantage in that it is more difficult to handle (e.g., in designing controllers), compared with the frequency domain. It is therefore important that the control engineer knows both the timeand the frequency-domain system’s behavior and is able to correlate the behavior of the system in these two domains. Chapter 8 aims to offer this knowledge, which is particularly necessary for Chap. 9. 8.2
FREQUENCY RESPONSE
Consider a SISO system with transfer function HðsÞ ¼
Kðs þ z1 Þðs þ z2 Þ ðs þ zm Þ ; ðs þ p1 Þðs þ p2 Þ ðs þ pn Þ
m
ð8:2-1Þ
Let the input be the sinusoidal function uðtÞ ¼ R sin !t. Then the response YðsÞ of the system will be Kðs þ z1 Þðs þ z2 Þ ðs þ zm Þ R! YðsÞ ¼ HðsÞUðsÞ ¼ ðs þ p1 Þðs þ p2 Þ ðs þ pn Þ s2 þ !2 Expand YðsÞ in partial fractions as follows: YðsÞ ¼
K K2 Kn K K þ þ þ þ nþ1 þ nþ2 s þ p1 s þ p2 s þ pn s þ j! s j!
The output yðtÞ ¼ L1 fYðsÞg will be 305
306
Chapter 8
yðtÞ ¼ K1 ep1 t þ K2 ep2 t þ þ Kn epn t þ Knþ1 ej!t þ Knþ2 ej!t Assume that all poles of HðsÞ lie in the left-half complex plane, i.e., Reðpi Þ < 0, 8i. Then the output of the system in the steady state will be yss ðtÞ ¼ Knþ1 ej!t þ Knþ2 e j!t Since Knþ1 ¼ lim ðs þ j!ÞYðsÞ ¼ s!j!
Hðj!ÞR 2j
and Knþ2 ¼ lim ðs j!ÞYðsÞ ¼ s!j!
Hð j!ÞR 2j
it follows that yss ðtÞ ¼
R ½Hðj!Þej!t þ Hð j!Þe j!t ¼ RjHð j!Þj sin½!t þ ’ð!Þ 2j
ð8:2-2Þ
where jHð j!Þj and ’ð!Þ are the amplitude and the phase of Hð j!Þ, respectively. From relation (8.2-2) it follows that when the transfer function of an SISO system has all its poles in the left-half complex plane, then the output of the system in the steady state, with sinusoidal excitation, is also sinusoidal. In particular, we observe that the amplitude of the output is the amplitude of the input multiplied by jHð j!Þj, while the phase of the output is the phase of the input shifted by ’ð!Þ. The output of the system in steady state, when excited by a sinusoidal function, i.e., yss ðtÞ of relation (8.2-2), is known as the frequency response of the system. Relation (8.2-2) has a significant characteristic in suggesting a relatively easy laboratory method to determine the transfer function Hð j!Þ of a system. Indeed, if a system is excited by a sinusoidal function with amplitude equal to unity and phase equal to zero, then the amplitude of the output steady state will be the amplitude of the transfer function, while the phase of the output steady state will be the phase of the transfer function. Therefore, if we excite Eq. (8.2-1) with uðtÞ ¼ sin !t and let ! take values for a certain range of frequencies, then we can readily sketch the curves of jHð j!Þj and ’ð j!Þ of the transfer function HðsÞ for this particular range of frequencies. This graphical representation of jHð j!Þj and ’ð j!Þ constitutes an important frequency domain tool for studying stability, specifying the closed-loop desired behavior, developing control design techniques, etc. For this reason, very often, even in cases where we know the analytical expression for the transfer function, we prefer to first determine its graphical representation and subsequently apply the graphical design methods. The above remarks can also be applied to MIMO systems whose transfer functions are matrices. In this case, the element hij ðsÞ of the transfer function matrix HðsÞ relates the ith output yi ðtÞ to the jth input uj ðtÞ ¼ Rj n !t, assuming that all other inputs are zero. In the steady state, the following relation holds ½ yi ðtÞss ¼ Rj jhij ð j!Þj sin½!t þ ’ij ð!Þ
ð8:2-3Þ
where jhij ð j!Þj is the amplitude and ’ij ð!Þ is the phase of the element hij ðsÞ of HðsÞ.
Frequency Domain Analysis
8.3 8.3.1
307
CORRELATION BETWEEN FREQUENCY RESPONSE AND TRANSIENT RESPONSE Characteristics of Frequency Response
The transfer function Hð j!Þ of Eq. (8.2-1) can be written as Hð j!Þ ¼ jHð j!Þj
Hð j!Þ ¼ M
’
where, for simplicity, we have placed M ¼ jHð j!Þj and ’ ¼
ð8:3-1Þ Hð j!Þ. Typical gra-
phical representations of M and ’, as functions of the frequency !, are given in Figure 8.1a and b, respectively. Certain characteristics of the amplitude curve M have special significance for the frequency response of a system. These characteristics are the following. 1 Resonant Peak Mp The value Mp is the maximum value of the amplitude M. As we will see in the Subsec. 8.3.4 (see Figure 8.9), large values of Mp usually correspond to large values of overshoot in the time domain, when the excitation is the step function. For most design problems, the value of Mp is chosen to lie between the values 1.1 and 1.5. 2 Resonant Frequency !p The resonant frequency !p is defined as the frequency for which Mð!p Þ ¼ Mp . 3 Bandwidth (BW) The bandwidth BW of the amplitude curve of Figure 8.1a is defined as the frequency !b for which Mð!b Þ ¼ 0:707. The BW in the frequency domain and the rise time Tr of a system’s response in the time domain (see Subsec. 4.2.2), are inversely proportional quantities. In the sequel, we will discuss the correlation between the frequency and time response of a system. In particular, we will study the correlation between the characteristics Mp , !p , and BW in the frequency domain with the characteristics T (time constant), ym (peak value), and (damping ratio) in the time domain. 8.3.2
Correlation for First-Order Systems
The transfer function of a first-order system has the form
Figure 8.1
Typical graphical representations of (a) the amplitude M and (b) the phase ’.
308
Chapter 8
HðsÞ ¼
K Ts þ 1
Clearly, HðsÞ in the sinusoidal steady state will be Hð j!Þ ¼
K K ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi j!T þ 1 !2 T 2 þ 1
tan1 !T
¼M
’
The waveforms of M and ’ are given in Figures 8.2a and 8.2b, respectively. Mp , !p , and BW are Mp ¼ K, !p ¼ 0, and BW ¼ !b ¼ 1=T. The above results in the frequency domain are correlated with those in the time domain (Subsec. 4.3.1) by the relation BW ¼ 1=T. This equation shows that BW and T are inversely proportional. 8.3.3
Correlation for Second-Order Systems
Consider a second-order system with transfer function HðsÞ ¼
!20 s þ 2!0 s þ !20 2
ð8:3-2Þ
The transfer function HðsÞ in the sinsusoidal steady state will be Hð j!Þ ¼
!20 1 ¼ 2 2 2 ð j!Þ þ 2!0 ð j!Þ þ !0 1 u þ j2u
ð8:3-3Þ
where, for simplicity, we have set u ¼ !=!0 . The amplitude M and the phase ’ of Hð j!Þ, according to Eq. (8.3-1), will be 1 2u 1 ð8:3-4Þ ; ’ ¼ tan M¼ 1 u2 ½ð1 u2 Þ2 þ ð2uÞ2 1=2 Next, we will determine !p , Mp , and BW of the system. To this end, to determine !p we differentiate M with respect to u and set the derivative equal to zero, i.e., dM ½2ð1 u2 Þu þ 42 u ¼ ¼0 du 2½ð1 u2 Þ2 þ ð2uÞ2 3=2 The above relation gives
Figure 8.2
Diagrams of (a) amplitude M and (b) phase ’ of a first-order system.
Frequency Domain Analysis
309
2u3 2u þ 42 u ¼ 0
ð8:3-5Þ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi The roots of Eq. (8.3-5) are u ¼ 0, u ¼ 1 22 , and u ¼ 1 22 . We disregard the third root since it has no physical meaning. Consider the root pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u ¼ 1 22 ; < 0:707 ð8:3-6Þ Its resonant frequency !p will be pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !p ¼ !0 1 22 ; < 0:707
ð8:3-7Þ
The resonant value Mp is found if we substitute Eq. (8.3-6) into Eq. (8.3-4) to yield Mp ¼
1 pffiffiffiffiffiffiffiffiffiffiffiffiffi ; 2 1 2
< 0:707
ð8:3-8Þ
From relations (8.3-7) and (8.3-8) it is clear that !p is a function of !0 and , while Mp is a function of only. Figure 8.3 presents typical shapes of the amplitude M as a function of the normalized frequency u and with free parameter the damping ratio . From Figure 8.3 it follows that for < 0:7, the peak value Mp increases as decreases. For 0:707, the peak value is Mp ¼ 1, which occurs when u ¼ 0. To determine the bandwidth BW, it suffices to specify the frequency !b . From Eq. (8.3-4) and the definition of bandwidth, we have Mð!b Þ ¼
½ð1
u2b Þ2
1 1 ¼ pffiffiffi 2 1=2 þ ð2ub Þ 2
where ub ¼ !b =!0 . If we solve Eq. (8.3-9) for ub , we obtain h pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffii1=2 ub ¼ 1 22 þ 44 42 þ 2
Figure 8.3
The plot of amplitude M of a second-order system versus u.
ð8:3-9Þ
ð8:3-10Þ
310
Chapter 8
Thus h pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi i1=2 BW ¼ !b ¼ !0 1 22 þ 44 42 þ 2
ð8:3-11Þ
From relation (8.3-10) we obseve that the normalized bandwidth ub is a function of the damping ratio only. The dependence of ub upon is given graphically in Figure 8.4. To correlate the frequency response with the time response, we make use of the results of Sec. 4.3. First, we compare the maximum value ym of the response yðtÞ when the excitation is the unit step function. In this case, ym is given by Eq. (4.3-10), i.e., by the relation h pffiffiffiffiffiffiffiffiffiffiffiffiffi i 0<<1 ym ¼ 1 þ exp = 1 2 ; The maximum value Mp of the amplitude of the function Hð j!Þ is given by Eq. (8.3-8). We remark that both ym and Mp are functions of only. In figure 8.5, ym and Mp are given as functions of . (8.3-7)) with the If we now compare the resonant frequency !p (see relation pffiffiffiffiffiffiffiffiffiffiffiffi ffi damped natural frequency !d (see Subsec. 4.3.2), where !d ¼ !n 1 2 , we observe that both frequencies are functions of !n and (Figure 8.6). Their ratio is given by pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !p 1 22 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi !d 1 2
8.3.4
Correlation for Higher-Order Systems
The analytical correlation between the frequency domain and the time domain response for higher-order systems is, in general, very difficult. This is because the time domain response yðtÞ and the frequency domain response Yð j!Þ are related by the Fourier integral, as follows:
Figure 8.4
The plot of the ratio Bw=!0 versus the damping constant .
Frequency Domain Analysis
Figure 8.5
311
The maximum amplitude Mp and the maximum value ym as a function of .
ð1 Yð j!Þ ¼
yðtÞej!t dt
ð8:3-12aÞ
Yð j!Þe j!t d!
ð8:3-12bÞ
1
yðtÞ ¼
1 2
ð1 1
The above Fourier transform pair requries great computational effort, particularly as the order of the system increases. Another reason which makes it difficult to extend the correlation between the frequency and time domain response to higherorder systems is that there is no analytical expression for the calculation of the poles of a higher-order algebraic equation. To circumvent this difficulty, it is usually proposed to approximate the original high-order transfer function HðsÞ with a new transfer function H ðsÞ of second order. If H ðsÞ approximates HðsÞ satisfactorily, then the study of the system is subsequently based on H ðsÞ, rather than HðsÞ, which makes the study of the system much simpler. Usually, we choose as poles of the new transfer function H ðsÞ the poles of HðsÞ which are closest to the imaginary
Figure 8.6
The plot of the ratio !p =!d versus the damping constant .
312
Chapter 8
axis. The issue of the approximation (simplification) of HðsÞ by H ðsÞ has been considered in Sec. 4.4. We close this sec. with Figure 8.7, 8.8, and 8.9 which refer to second- and higher-order systems and give an overall picture of the correlation between the behavior of a system in the time domain and frequency domain. In Figure 8.7 the correlation between the time domain and frequency domain is given, where the description in the frequency domain is given both with the curves M and ’, as well as with the Nyquist diagrams (see next section). Figure 8.8 gives an overall picture of the correlation between the rise time Tr and the bandwidth BW, from which we reach the conclusion that Tr and BW are inversely proportional. Finally, Figure 8.9 gives the curves related to the overshoot ym and the resonance value Mp , from which we reach the conclusion that as the resonance value Mp increases, the
Figure 8.7 Nyquist diagram, amplitude curves of M and ’, and the output yðtÞ for stable and unstable systems. (a) Stable system without oscillations; (b) stable system with oscillations; (c) stable system with sustained oscillations; (d) unstable system.
Frequency Domain Analysis
313
Figure 8.8 (a) Amplitude M and (b) output yðtÞ curves of two different systems which show the relation between Tr and bandwidth BW.
Figure 8.9 (a) The amplitude M and (b) the output yðtÞ of four different systems, which show the relation between ym and Mp .
314
Chapter 8
overshoot ym also increases. In practice, it is widely accepted that a system has a good performance if the value for Mp lies between 1.1 and 1.5, with special preference to the value Mp ¼ 1:3.
8.4
THE NYQUIST STABILITY CRITERION
8.4.1
Introduction
The Nyquist stability criterion refers to the stability of closed-loop systems. In cases where a mathematical model is not available and the system transfer function is described graphically, then the use of the Nyquist stability criterion comes in very handy. In cases where a mathematical model is available, then one can apply one of the algebraic criteria to readily determine the stability of the closed-loop system. However, even in this latter case, one can also apply the Nyquist stability criterion to acquire further useful knowledge about the behavior of the closed-loop system. Some important advantages of the Nyquist criterion are given below. 1. It gives information, not only about the stability but also about the relative stability of the system. The meaning of the term relative stability may be given on the basis of the roots of the closed-loop characteristic polynomial pc ðsÞ in the complex plane as follows. Let all roots of pc ðsÞ lie in the left-half complex plane. Then, as the roots of pc ðsÞ move to the right and hence closer to the imaginary axis (from left to right), the system becomes less stable, and as the root of pc ðsÞ move to the left and hence away from the imaginary axis, the system becomes more stable. 2. It gives information about the behavior (the performance) of the system in the time domain. 3. It can be used for the study of the stability of other categories of systems, such as time-delay systems, nonlinear systems, etc. Consider the closed-loop system of Figure 8.10. The transfer function HðsÞ of the closed-loop system is given by HðsÞ ¼
GðsÞ GðsÞ ¼ 1 þ GðsÞFðsÞ WðsÞ
ð8:4-1Þ
where WðsÞ ¼ 1 þ GðsÞFðsÞ Assume that the open-loop transfer function GðsÞFðsÞ has the form
Figure 8.10
Block diagram of a closed-loop system.
ð8:4-2Þ
Frequency Domain Analysis
GðsÞFðsÞ ¼
Kðs þ z1 Þðs þ z2 Þ ðs þ zm Þ ; s j ðs þ p1 Þðs þ p2 Þ ðs þ pq Þ
315
where j þ q ¼ n
ð8:4-3Þ
Substituting expression (8.4-3) in expression (8.4-2) yields WðsÞ ¼ 1 þ GðsÞFðsÞ
ð8:4-4Þ
or WðsÞ ¼
s j ðs þ p1 Þðs þ p2 Þ ðs þ pq Þ þ Kðs þ z1 Þðs þ z2 Þ ðs þ zm Þ pc ðsÞ ¼ p ðsÞ s j ðs þ p1 Þðs þ p2 Þ ðs þ pq Þ ð8:4-5Þ
The polynomials pc ðsÞ and p ðsÞ are the characteristic polynomials of the systems with transfer functions HðsÞ and GðsÞFðsÞ, respectively. It is important to note that the stabilities of the closed-loop system and of the system with transfer function GðsÞ FðsÞ are not correlated. For example, if a system with transfer function GðsÞFðsÞ is unstable, this does not necessarily mean that the closed-loop system is also unstable. The study of the stability of closed-loop systems through the use of algebraic criteria is based on the characteristic polynomial pc ðsÞ, whereas the Nyquist criterion is based on the graphical representation of the open-loop transfer function GðsÞFðsÞ, for s 2 s , where s is a specific closed path in the s-plane. This graphical representation of GðsÞFðsÞ gives information not only about the stability but also about the relative stability of the system. In order to formulate the Nyquist criterion, a certain mathematical background on complex function theory is necessary and it is given below.
8.4.2
Background Material on Complex Function Theory for the Formulation of the Nyquist Criterion
Consider a complex function WðsÞ and let s be an arbitrary closed path in the splane (Figure 8.11a). Then, for s 2 s , the complex function WðsÞ (except for some special forms of WðsÞ which will not be considered here), will also form a closed path W in the WðsÞ plane (Figure 8.11b). If in the closed path s we give a particular direction, then the path W will also have a certain direction, not necessarily the same as the direction of s (see Figure 8.12). Furthermore, if the path s is a simple closed path, then the path W will not, in general, be a simple closed path but can be, as for example, in Figure 8.12a and d. Of course, the particular form of the path W depends not only on the path s but also on the particular form of the function WðsÞ. Let the complex function WðsÞ have the form WðsÞ ¼ K
ðs þ z1 Þðs þ z2 Þ ðs þ zm Þ ; ðs þ p1 Þðs þ p2 Þ ðs þ pn Þ
K >0
ð8:4-6Þ
Also let the closed path s enclose P poles and Z zeros of WðsÞ. Then, the numbers P and Z play a very decisive role in the shape of the closed path W , as stated by the following theorem.
316
Figure 8.11
Chapter 8
The closed path W of WðsÞ for s 2 s .
Theorem 8.4.1 Let the complex function WðsÞ be a single-valued, rational function of the form (8.46) and also assume that this function is analytical (i.e., all its derivatives exist) in all points of a closed path s . Then, the corresponding path W will encircle the origin N times, where N ¼ Z P. If N > 0 ðN < 0Þ then the closed path W has the same (the opposite) direction as that of the closed path s .
Figure 8.12
Various possible closed paths W of WðsÞ.
Frequency Domain Analysis
317
Instead of giving a strict mathematical proof, we will give a rather simple and practical explanation of Theorem 8.4.1. To this end, we write WðsÞ as follows: WðsÞ ¼ jWðsÞj
ð8:4-7Þ
WðsÞ
where jWðsÞj ¼ amplitude of WðsÞ ¼ K WðsÞ ¼ phase of WðsÞ ¼
m X
js þ z1 jjs þ z2 j js þ zm j js þ p1 jjs þ p2 j js þ pn j ’i ð!Þ
i¼1
where ’i ð!Þ ¼
s þ zi and i ð!Þ ¼
n X
i ð!Þ
ð8:4-8Þ ð8:4-9Þ
i¼1
s þ pi . The positions of the zeros z1 ; z2 ; . . .
; zm and of the poles p1 ; p2 ; . . . ; pn of WðsÞ are depicted in Figure 8.13. Choose the closed path s such as to encircle Z zeros and P poles of WðsÞ. Also, let the point s 2 s move on s , having the direction of the arrow (i.e., counterclockwise). Then, we observe that as the point s travels around the closed path s , each factor s þ zk or s þ pi will generate an angle of 3608 when zk or pi lie within the closed path s , or an angle of 08 when zk or pi lie outside the closed path s . Since the phase of WðsÞ, according to the relation (8.4-9), is the sum of the phases of the factors s þ zk of the numerator minus the sum of the phases of the factors s þ pi of the denominator, it follows that the total phase ’ which the transfer function WðsÞ will generate as the point s travels around the closed path s will be ’¼
WðsÞ ¼ 2ðZ PÞ ¼ 2N
ð8:4-10Þ
To determine the number N from the diagram of WðsÞ, we draw an arbitrary radius from the origin to infinity. Then, the diagram of WðsÞ will intersect this radius Z times in the direction of s and P times in the opposite direction. Therefore N ¼ Z P. Certain illustrative examples are given in Figure 8.12.
Figure 8.13
The closed path s which enclosed Z zeros and P poles of WðsÞ.
318
8.4.3
Chapter 8
The Nyquist Stability Criterion
The Nyquist stability criterion is based on Theorem 8.4.1. Since the objective of the Nyquist criterion is the study of the stability of the closed-loop system, the closed path s is no longer arbitrary. It is that particular path which encloses, in the clockwise direction, the entire right-half complex plane (Figure 8.14). This special path is called the Nyquist path and it is designated by N . If the function WðsÞ has poles on the imaginary axis, then the Nyquist path circumvents these poles by going around them on a semicircle with infinitesimal radius ! 0 (Figure 8.14). This is done in order that the function WðsÞ remains analytcial on the Nyquist path and, hence, Theorem 8.4.1 holds. The path W of WðsÞ, which corresponds to the Nyquist path, is called the Nyquist diagram of WðsÞ. Assume that WðsÞ is the transfer function of a system. Then, it is clear that in the case of a stable system there must be P ¼ 0, since there are no poles in the righthalf complex plane. This means that N ¼ Z, i.e., the Nyquist diagram of a stable system must encircle the origin clockwise as many times as the number of zeros Z of WðsÞ which lie in the right-half complex plane. Now let WðsÞ be given by the expression (8.4-5), i.e., by the expression WðsÞ ¼ 1 þ GðsÞFðsÞ ¼
pc ðsÞ p ðsÞ
ð8:4-11Þ
where pc ðsÞ is the characteristic polynomial of the closed-loop transfer function HðsÞ and p ðsÞ is the characteristic polynomial of the open-loop transfer function GðsÞFðsÞ. Also assume that WðsÞ has Z zeros and P poles within the Nyquist path N . Apparently, for the closed-loop system to be stable, the roots of pc ðsÞ must lie in the left-half complex plane, i.e., there must be Z ¼ 0. This means that for the closedloop system to be stable, the Nyquist diagram of WðsÞ, having the same direction as that of the Nyquist path N , encircles the origin N ¼ P times. If P ¼ 0, then the
Figure 8.14
The Nyquist path N .
Frequency Domain Analysis
319
Nyquist diagram of WðsÞ does not encircle the origin. Therefore, we have established the following theorem, which is the well-known Nyquist theorem. Theorem 8.4.2 Assume that the transfer function WðsÞ ¼ 1 þ GðsÞFðsÞ does not have poles in the right-half complex plane. Then, for the closed-loop system (8.4-1) to be stable, the Nyquist diagram of WðsÞ, having the same direction as that of the path N , must not encircle the origin. Define W ðsÞ ¼ WðsÞ 1 ¼ GðsÞFðsÞ
ð8:4-12Þ
Then, the Nyquist diagrams of WðsÞ and W ðsÞ differ in that the diagram for W ðsÞ has been translated by one unit to the left of the diagram for WðsÞ. This means that the conclusions about the stability of WðsÞ hold for W ðsÞ as well, if the origin is now substituted by the point ð1; j0Þ, which is called the critical point. Hence, the following theorem holds. Theorem 8.4.3 Assume that the transfer function W ðsÞ ¼ GðsÞFðsÞ does not have poles in the righthalf complex plane. Then, for the closed-loop system (8.4-1) to be stable, the Nyquist diagram of GðsÞFðsÞ, having the same direction as that of the path N , does not encircle the critical point ð1; j0Þ. As an introductory illustrative example of Theorem 8.4.3, we will study the stability of the two closed-loop systems which have as Nyquist diagrams the Nyquist diagrams of the open-loop transfer functions GðsÞFðsÞ of Figure 8.15. It is assumed that the transfer function GðsÞFðsÞ does not have any poles in the right-half complex plane. In Figure 8.15a, we observe that the Nyquist diagram of GðsÞFðsÞ does not encircle clockwise the critical point ð1; j0Þ, while it does in Figure 8.15b. Hence the
Figure 8.15
Nyquist diagrams of (a) a stable and (b) an unstable system.
320
Chapter 8
closed-loop system, having as Nyquist diagram of the open-loop transfer function GðsÞFðsÞ that of Figure 8.15a, is stable, while that of Figure 8.15b is unstable. We introduce the following definition. Definition 8.4.1 A transfer function HðsÞ is called a minimum phase function when it does not have any poles or zeros in the right-half complex plane. In the case where it does, then the complex function HðsÞ is called a nonminimum phase function. Using all the material we have presented up to now regarding the Nyquist criterion, we have essentially proven the following theorem. Theorem 8.4.4 Assume that the transfer function W ðsÞ ¼ GðsÞFðsÞ is a nonminimum phase function and has P poles in the right-half complex plane. Then, for the closed-loop system (8.4-1) to be stable, the Nyquist diagram of GðsÞFðsÞ, having the opposite direction to that of the path N , must encircle P times the critical point ð1; j0Þ. Theorem 8.4.4 is called the generalized Nyquist theorem. It is clear that Theorem 8.4.3 is a special case of Theorem 8.4.4. As an introductory illustrative example of Theorem 8.4.4 we will study the following open-loop transfer functions: W1 ðsÞ ¼ GðsÞFðsÞ ¼
K sðTs 1Þ
and
W2 ðsÞ ¼ GðsÞFðsÞ ¼
KðT10 s þ 1Þ sðT1 s 1Þ ð8:4-13Þ
The Nyquist diagrams of W1 ðsÞ and W2 ðsÞ are given in Figure 8.16. For the transfer function W1 ðsÞ, we have that P ¼ 1 (because W1 ðsÞ has the pole 1=T in the right-half complex plane). According to Theorem 8.4.4, for the closed-loop system to be stable, the Nyquist diagram must encircle the critical point ð1; j0Þ once in the counterclockwise direction. From Figure 8.16a it follows that the Nyquist diagram encircles the critical point but in the clockwise direction and, hence, the closed-loop system is unstable. In this example we observe that both the open-loop system and the closedloop system are unstable. For the transfer function W2 ðsÞ we also have that P ¼ 1. The Nyquist diagram for small values of K does not encircle, in the counterclockwise direction, the point ð1; j0Þ and, therefore, the closed-loop system is unstable (see Figure 8.16b). For greater values of K, the Nyquist diagram encircles the critical point ð1; j0Þ once in the counterclockwise direction and, hence, the closed-loop system is stable (see Figure 8.16c). In this example we observe that even though the open-loop system is unstable, the closed-loop system can be stable, provided that K assumes large enough values. 8.4.4
Construction of Nyquist Diagrams
Since the Nyquist diagram of the open-loop transfer function GðsÞFðsÞ constitutes the basis for the application of the Nyquist criterion, it follows that one must be familiar with how to construct such diagrams. To this end, this subsection presents a rather systematic approach on how to construct Nyquist diagrams of various forms of transfer functions.
Frequency Domain Analysis
321
Nyquist diagrams of nonminimum phase systems. (a) Plot of W1 ðsÞ (unstable system); (b) plot of W2 ðsÞ for small values of K (unstable system); (c) plot of W2 ðsÞ for large values of K (stable system).
Figure 8.16
1
Nyquist Diagram for First-Order Systems
Consider the first-order system with transfer function HðsÞ ¼
1 Ts þ 1
ð8:4-14Þ
where, for simplicity, we use HðsÞ instead of GðsÞFðsÞ. Since HðsÞ does not have any poles on the imaginary axis, it follows that the Nyquist path N will cover the entire imaginary axis. Therefore, the Nyquist diagram of HðsÞ is the function
322
Chapter 8
Hð j!Þ ¼
1 ; j!T þ 1
! 2 ð1; þ1Þ
ð8:4-15Þ
We have Hð j!Þ ¼ jHð j!Þj
1 ’ð!Þ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ! T2 þ 1
tan1 !T
If we let ! ¼ 0, then jHð j!Þj ¼ 1 and ’ð!Þ ¼ 08. As the frequency ! increases, the amplitude jHð j!j becomes smaller while the phase ’ð!Þ becomes more negative. In the limit where ! ! 1, the amplitude jHð j!j ! 0, whereas the phase ’ð!Þ ! 908. Finally, we observe that jHð j!Þj ¼ jHðj!Þj and ’ð!Þ ¼ ’ð!Þ. Hence, the Nyquist diagram is symmetrical with respect to the Re Hð j!Þ axis and has the form given in Figure 8.17. It is mentioned that the determination of the analytical expression of the Nyquist diagram with coordinates Re H and Im H is difficult, particularly as the order of the system becomes greater. For the case of first-order systems, the determination of the analytical expression of the Nyquist diagram is relatively simple and is derived as follows. We have Hð j!Þ ¼
1 1 !T ¼ j 2 2 ¼ x þ jy j!T þ 1 !2 T 2 þ 1 ! T þ1
where x ¼ Re Hð j!Þ ¼
1 !2 T 2
þ1
;
y ¼ Im Hð j!Þ ¼
!T þ1
!2 T 2
Let u ¼ !T. then x¼
1 ; u2 þ 1
y¼
u u2 þ 1
Since y ¼ ux, it follows that
Figure 8.17
The Nyquist diagram of a first-order transfer function.
Frequency Domain Analysis
y u xy x ¼ h i2 y¼ 2 ¼ 2 y u þ1 x þ y2 þ1 x
323
or
y2 þ x2 x ¼ 0
We finally have 1 2 1 ¼ y2 þ x 2 4
ð8:4-16Þ
Relation (8.4-16) is the equation of a circle with its center at the point ð1=2; 0Þ and with radius 1/2 and constitutes the analytical expression of the Nyquist diagram of Eq. (8.4-14). The circle (8.4-16) is shown in Fig. 8.17. 2
Nyquist Diagram for Second-Order Systems
Consider a second-order system with transfer function HðsÞ ¼
!2n s2 þ 2!n s þ !2n
Since HðsÞ does not have any poles on the imaginary axis, it follows that the Nyquist path will cover the entire imaginary axis. Therefore, the Nyquist diagram of HðsÞ is Hð j!Þ ¼
!2n ; ð j!Þ2 þ 2!n ð j!Þ þ !2n
! 2 ð1; 1Þ
ð8:4-17Þ
We have Hð j!Þ ¼ jHð j!Þj
!2n ffi ’ð!Þ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð!2n !2 Þ2 þ 42 !2n !2
tan1 ½2!n !=ð!2n !2 Þ
As the frequency ! varies from 0 to 1, the Nyquist diagram starts with amplitude 1 and zero phase and terminates with amplitude 0 and phase 1808. For ! ¼ !n , the amplitude is ð2Þ1 and the phase is 908. In Figure 8.18 the Nyquist diagram of HðsÞ is given only for ! 2 ð0; 1Þ. The rest of the diagram for ! 2 ð0; 1Þ is omitted since it is symmetrical to the Nyquist diagram for ! 2 ð0; þ1Þ with respect to the Re H axis. 3
Nyquist Diagrams for Higher-Order Systems
The construction of Nyquist diagrams for systems of third, fourth, etc., order cannot be constructed easily, as opposed to the cases of first- and second-order systems, unless the numerator and the denominator of the transfer function can be factored out. For this last case, very interesting results may be derived, as shown in the sequel. 4
Nyquist Diagrams for Transfer Functions with Poles on the Imaginary Axis
When a transfer function has poles on the imaginary axis, the construction of the Nyquist diagrams must be carried out with extra attention, as shown in the following introductory example.
324
Chapter 8
Figure 8.18
The Nyquist diagram of a second-order system.
Example 8.4.1 Consider a system with transfer function HðsÞ ¼
K sðTs þ 1Þ
ð8:4-18Þ
Construct the Nyquist diagram. Solution Since HðsÞ has a pole at s ¼ 0 on the imaginary axis, it follows that the path N will cover the entire imaginary axis, except the point s ¼ 0 (see Figure 8.19a). To facil-
Figure 8.19
The Nyquist diagram of a system with a pole on the imaginary axis. (a) The Nyquist path; (b) the Nyquist diagram of Eq. (8.4-18).
Frequency Domain Analysis
325
itate the construction of the Nyquist diagram of HðsÞ we divide the Nyquist path N into four segments and construct the Nyquist diagram for each of the corresponding four segments of N as follows. a. The segment for s 2 ð j0þ ; j1Þ. This segment of the path N is the positive part of the imaginary axis not including the origin. For this range of frequencies the transfer function is given by Hð j!Þ ¼
K ; j!ð j!T þ 1Þ
! 2 ð0þ ; 1Þ
We have K ’ð!Þ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ! ! T2 þ 1
Hð j!Þ ¼ jHð j!Þj
908 tan1 !T
As the frequency ! varies from 0þ to þ1, the amplitude jHð j!Þj varies from 1 to 0 and the phase ’ð!Þ from 908 to 1808 (see Figure 8.19b). b. The segment for s 2 ð j1; j1Þ, or s ¼ Re j , where R ! 1 and 2 ð908; 908Þ. This segment is the large semicircle of the path N . In this case, the transfer function becomes lim HðRe j Þ ¼ lim
R!1
R!1
K K ¼ lim ej2 ¼ lim R!1 TR2 e j2
!0 Re j ðTRe j þ 1Þ
This means that as the phase of the semicircle Re j takes on values in the clockwise direction from 908 to 908, the Nyquist diagram of the transfer function HðsÞ moves counterclockwise, generating a phase change of 3608 from 1808 to 1808. Therefore, when s ¼ Re j , where R ! 1 and 2 ð908; 908Þ, the transfer function HðsÞ is given by HðsÞ ¼ ej2 , where ! 0. Hence, the large semicircle of the path N maps onto a small circle around the origin in the HðsÞ plane. c. The segment for s 2 ðj1; j0 Þ. Since this segment is symmetric to the segment s 2 ðj0þ ; j1Þ with respect to the real axis, it follows that the corresponding Nyquist diagram will be symmetric with respect to the Re H axis. d. The segment for s 2 ð j0 ; j0þ Þ, or s ¼ e j , where ! 0 and 2 ð908; 908Þ. This segment is the small semicircle, where the path N moves around the pole s ¼ 0. In this case the transfer function becomes lim Hð e jÞ ¼ lim
!0
!0
K ¼ lim Rej R!1
e j ðT e j þ 1
This means that as the phase of the semicircle e j takes on values in the counterclockwise direction from 908 and 908, the Nyquist diagram of the transfer function moves clockwise, generating an angle of 1808 from 908 to 908. Therefore, as s ¼ e j , where ! 0 and 2 ð908; 908Þ, the transfer function HðsÞ is given by HðsÞ ¼ Rej , where R ! 1. Hence, the small interior semicircle of the path N maps onto the big semicircle in the right-half HðsÞ plane.
326
Chapter 8
5 The Influence on the Nyquist Diagram of Adding Poles and Zeros to the Transfer Function Consider the transfer function H1 ðsÞ ¼
K T1 s þ 1
Assume that the transfer function H1 ðsÞ is multiplied by the factor ðT2 s þ 1Þ1 , i.e., a pole is added to the transfer function at the point s ¼ 1=T2 . Then, the resulting transfer function H2 ðsÞ is given by H2 ðsÞ ¼
K ðT2 s þ 1ÞðT1 s þ 1Þ
ð8:4-19Þ
The Nyquist diagrams of H1 ðsÞ and H2 ðsÞ are given in Figure 8.20a and b, respectively. From these two diagrams, the influence of adding a pole to the transfer function H1 ðsÞ is clear. If we add one or two more poles to the original transfer function H1 ðsÞ, the Nyquist diagram will be as in Figures 8.20c and d. In cases where the added poles are at the origin, or some are at the origin and some are not, then the Nyquist diagram of H1 ðsÞ is influenced as in Figures 8.20e–h and 8.21a–d, respectively. On the basis of Figures 8.20 and 8.21a–d, one may derive the following general conclusion: if a new pole is added to a transfer function, then the new Nyquist diagram occupies the next quadrant which lies in the counterclockwise direction. For the particular case where the new pole is at the origin, then the new Nyquist diagram occupies the next quadrant, in the counterclockwise direction, but it no longer occupies the starting quadrant. It is almost like ‘‘rotating’’ the Nyquist diagram by one quadrant in the counterclockwise direction. In the general case, where HðsÞ ¼ K
ðT10 s þ 1ÞðT20 s þ 1Þ s j ðT1 s þ 1ÞðT2 s þ 1Þ
the Nyquist diagrams for systems of type 0, 1, 2, and 3, which corresponds to the values of j ¼ 0, 1, 2, and 3, are given in Figure 8.22. From the Figures 8.20, 8.21a–d, and 8.22 it becomes clear that adding poles to the transfer function of a system results in a system that is less stable or might even result in an unstable system. Now, consider multiplying the transfer function H1 ðsÞ with the factor ðT 0 s þ 1Þ, i.e., a zero is added to the transfer function at the point s ¼ 1=T 0 . Then the new transfer function will be H2 ðsÞ ¼ K
T 0s þ 1 T2 s þ 1
ð8:4-20Þ
The Nyquist diagram of H2 ðsÞ is given in the Figure 8.21e, where the influence of adding a zero to the transfer function is clear. Other examples which illustrate the influence of adding zeros to the Nyquist diagrams are given in Figures 8.21f–h. On the basis of these figures one may arrive at the general conclusion that adding zeros results in a more stable system. It is remarked once again that, for the sake of simplicity, in all material of the present subsection we have been using HðsÞ instead of GðsÞFðsÞ. From this point on, we return to the regular notation GðsÞFðsÞ for the open-loop transfer function.
Frequency Domain Analysis
Figure 8.20
Nyquist diagrams. Influence of additional poles.
K ðT1 s þ 1Þ K ðbÞ HðsÞ ¼ ðT2 s þ 1ÞðT1 s þ 1Þ K ðcÞ HðsÞ ¼ ðT3 s þ 1ÞðT2 s þ 1ÞðT1 s þ 1Þ K ðdÞ HðsÞ ¼ ðT4 s þ 1ÞðT3 s þ 1ÞðT2 s þ 1ÞðT1 s þ 1Þ
ðaÞ
HðsÞ ¼
327
K ðTs þ 1Þ K ðfÞ HðsÞ ¼ sðTs þ 1Þ K ðgÞ HðsÞ ¼ 2 s ðTs þ 1Þ K ðhÞ HðsÞ ¼ 3 s ðTs þ 1Þ
ðeÞ HðsÞ ¼
328
Figure 8.21
Chapter 8
Nyquist diagrams. Influence of additional poles and zeros.
K sðT2 s þ 1ÞðT1 s þ 1Þ K ðbÞ HðsÞ ¼ 2 s ðT2 s þ 1ÞðT1 s þ 1Þ K ðcÞ HðsÞ ¼ sðT3 s þ 1ÞðT2 s þ 1ÞðT1 s þ 1Þ K ðdÞ HðsÞ ¼ 2 s ðT3 s þ 1ÞðT2 s þ 1ÞðT1 s þ 1Þ
ðaÞ HðsÞ ¼
T 0s þ 1 ðT1 s þ 1Þ T 0s þ 1 ðfÞ HðsÞ ¼ K sðT1 s þ 1Þ T 0s þ 1 ðgÞ HðsÞ ¼ K sðT1 þ sÞðT2 s þ 1Þ T 0s þ 1 ðhÞ HðsÞ ¼ K 2 s ðT1 s þ 1Þ
ðeÞ
HðsÞ ¼ K
Frequency Domain Analysis
Figure 8.22
329
Influence of the system’s type on the Nyquist diagrams.
In closing the issue of constructing the Nyquist diagram of a system, we point out the following with regard to the influence of the gain K. For first- and secondorder systems, the closed-loop system is always stable for all values of K. However, for third- or higher-order systems, the stability is greatly influenced by the gain K. In the general case one may conclude that, as the gain K is increased, the closed-loop system tends to become unstable. This becomes particularly clear for the case of third-order systems for which the open-loop transfer function GðsÞFðsÞ has the form GðsÞFðsÞ ¼
K ðT1 s þ 1ÞðT2 s þ 1ÞðT3 s þ 1Þ
From Figure 8.23 it is clear that as K increases the closed-loop system becomes unstable. In particular, when K ¼ K1 the closed-loop system is stable, when K ¼
Figure 8.23
The Nyquist diagram of a third-order system as K increases.
330
Chapter 8
K2 the closed-loop system undergoes sustained oscillations, and for K ¼ K3 the closed-loop system is unstable. There are, however, cases where the closed-loop system is stable only for certain ranges of values of the gain K. As an example, consider the system having the following open-loop transfer function: GðsÞFðsÞ ¼
KðT 0 s þ 1Þ2 ðT1 s þ 1ÞðT2 s þ 1ÞðT3 s þ 1ÞðT4 s þ 1Þ2
The Nyquist diagram of GðsÞFðsÞ is given in Figure 8.24. From this figure it follows that the closed-loop system can become unstable not only as K increases but also as K decreases. In this case, the closed-loop system is stable only for certain ranges of values of the gain K. 8.4.5
Gain and Phase Margins
Consider the Nyquist diagrams of Figure 8.25 of a certain open-loop transfer function GðsÞFðsÞ having the form (8.4-3). We introduce the following definitions. Definitions 8.4.2 Let !c be the critical frequency where the Nyquist diagram of GðsÞFðsÞ intersects the Re GF -axis. Then, the gain margin Kg of the closed-loop system is given by the following relationship: Kg ðdBÞ ¼ 20 log10 jGð j!c ÞFðj!c Þj
ð8:4-21Þ
The physical meaning of the gain margin Kg may be interpreted as follows. The gain margin Kg is the gain in decibels (dB) which the open-loop transfer function is allowed to increase before the closed-loop system becomes unstable. This increase is usually done by increasing the gain K of the open-loop system.
Figure 8.24
The Nyquist diagram of a system which becomes unstable not only as K increases but also as K decreases.
Frequency Domain Analysis
Figure 8.25
331
Diagrams of the gain and phase margin of (a) a stable and (b) unstable system.
Definition 8.4.3 Let ! 0 be the critical frequency where the amplitude of the Nyquist diagram of GðsÞFðsÞ is equal to unity, i.e., jGð j! 0 ÞFð j! 0 Þj ¼ 1. Also, let ’ be the phase of Gð j! 0 ÞFð j! 0 Þ. Then, the phase margin ’p of the closed-loop system is given by the following relationship: ’p ¼ 1808 þ ’
ð8:4-22Þ
The physical meaning of the phase margin ’p may be interpreted as follows: the phase margin ’p is the phase in degrees which the Nyquist diagram of Gð j!ÞFð j!Þ must rotate about the origin until the point where jGð j!ÞFð j!Þj ¼ 1 passes through the critical point ð1; j0Þ. This change in phase is usually done by varying the parameters of the system, as for example the damping ratio, the time constants, etc., and not by varying the gain K of the system. From Definitions 8.4.2 and 8.4.3 it follows that the phase margin ’p and the gain margin Kg give an indication of how close the Nyquist diagram of GðsÞFðsÞ is to the critical point ð1; j0Þ. Therefore, ’p and Kg give pertinent information regarding the relative stability of the closed-loop system. For this reason, they constitute important design criteria. The stability of the closed-loop system may readily be determined by the signs of ’p and Kg . Indeed, the closed-loop is stable when the margins ’p and Kg are both positive (Figure 8.25a). On the contrary, a closed-loop system is unstable when one or both margins ’p and Kg are negative (Figure 8.25b). The relative stability of a stable closed-loop system is directly related to the values of ’p and Kg . As the values of ’p and Kg become greater, the closed-loop systems becomes more stable. That is to say that the greater the margins ’p and Kg , the less probable it is for the system to become unstable. All the above results hold for systems whose open-loop transfer function GðsÞFðsÞ is of minimum phase (Definition 8.4.1). However, in cases where GðsÞFðsÞ is of nonminimum phase the above results do not hold because one of the margins may be negative even though the closed-loop system is stable (see Example 8.4.5).
332
Chapter 8
Example 8.4.2 Consider the third-order system with open-loop transfer function GðsÞFðsÞ ¼
K ; sðT1 s þ 1ÞðT2 s þ 1Þ
K; T1 ; T2 > 0
Construct the Nyquist diagram and study the stability of the closed-loop system. Solution For s 2 ðj0þ ; j1Þ, we have Gð j!ÞFð j!Þ ¼
K ¼ x þ jy j!ð j!T1 þ 1Þð j!T2 þ 1Þ
where x¼
KðT1 þ T2 Þ 2 1 þ ! ðT12 þ T22 Þ þ !4 T12 T22
y¼
and
K!1 ð!2 T1 T2 1Þ 1 þ !2 ðT12 þ T}2 Þ þ !4 T12 T22
The point where the Nyquist diagram intersected the real axis is when y ¼ 0, i.e., when K!1 ð!2 T1 T2 1Þ ¼ 0. The roots of this equation are 1 ! ¼ pffiffiffiffiffiffiffiffiffiffiffi ; T1 T2
1 ! ¼ pffiffiffiffiffiffiffiffiffiffiffi T1 T2
!¼1
and
The negative value of ! is rejected because itffi has no physical meaning. The transfer pffiffiffiffiffiffiffiffiffiffi function Gð j!ÞFð j!Þ, for ! ¼ !c ¼ 1= T1 T2 , becomes T1 T2 Gð j!c ÞFð j!c Þ ¼ x ¼ K T1 þ T2 For the closed-loop system to be stable there must be x > 1, or equivalently T1 T2 T1 þ T2 > 1 or 0
K ; s2 ðTs þ 1Þ
K; T > 0
Construct the Nyquist diagram and study the stability of the closed-loop system. Solution For s 2 ð j0þ ; j!Þ, we have Gð j!ÞFð j!Þ ¼
K ¼ x þ jy !2 ð j! þ 1Þ
where x¼
K !2 ð!2 T 2
þ 1Þ
and
y¼
KT !ð!2 T 2
þ 1Þ
Frequency Domain Analysis
Figure 8.26
333
Nyquist diagram of Example 8.4.2.
The point where the Nyquist diagram intersects the x-axis is when y ¼ 0, i.e., when ! ! 1. Therefore, the Nyquist diagram has the form of Figure 8.27. Hence there is no value of K for which the closed-loop system is stable, since the Nyquist diagram encircles clockwise the point ð1; j0Þ permanently. Example 8.4.4 Consider the numerical control tool machine described in Sec. 1.4 (see Figure 1.19). A simplified block diagram of the closed-loop system is given in Figure 8.28. The
Figure 8.27
Nyquist diagram of Example 8.4.3.
334
Chapter 8
Figure 8.28
Block diagram of the numerical control tool machine.
transfer function Gh ðsÞ of the hydraulic servomotor has been determined in Subsec. 3.12.6. For the servomotor, let K1 ¼ 1 and a ¼ 1. For the amplifier controller circuit transfer function Gc ðsÞ, let L ¼ 1 and R ¼ 4. Then, the open-loop transfer function becomes GðsÞFðsÞ ¼ Gc ðsÞGh ðsÞFðsÞ ¼
K ; sðs þ 1Þðs þ 4Þ
K>0
Determine: (a) The range of values of K for which the closed-loop system is stable (b) The gain margin Kg when K ¼ 2 and when K ¼ 40. Solution (a) For s 2 ð j!þ ; j1Þ, we have Gð j!ÞFð j!Þ ¼
K ðK=4Þ ¼ j!ð j! þ 1Þð j! þ 4Þ j!ð j! þ 1Þð j!=4 þ 1Þ
If we apply the results of Example 8.4.2, we have 1 1 !c ¼ pffiffiffiffiffiffiffiffiffiffiffi ¼ pffiffiffiffiffiffiffiffiffiffiffi ¼ 2 T1 T2 ð1=4Þ Therefore, the closed-loop system is stable when 0<
K T1 þ T2 < 4 T1 T2
or
0<
K 1 þ 1=4 < 4 1=4
or
0 < K < 20
Indeed, for ! ¼ 2, the Nyquist diagram of Gð j!ÞFð j!Þ intersects the real axis and Gð j2ÞFð j2Þ ¼
K 20
The critical value of K for which Gð j2ÞFð j2Þ ¼ 1, is K ¼ 20. Therefore, for K 2 ð0; 20Þ the closed-loop system is stable. (b). For K ¼ 2, and upon using definition (8.4-21), we have 2 Kg ðdBÞ ¼ 20 log10 ¼ 20 ðdBÞ 20 For K ¼ 40, we have
Frequency Domain Analysis
335
Figure 8.29 Nyquist diagram of a machine tool control system. (a) Stable: K < 20; (b) unstable: K > 20. 40 Kg ðdBÞ ¼ 20 log10 ¼ 20 log 2 ðdBÞ 20 Therefore, for K ¼ 2 the closed-loop system is stable, with gain margin Kg ¼ 20 dB, whereas when K ¼ 40 the closed-loop system is unstable. In Figure 8.29 the Nyquist diagrams of the open-loop transfer function are given for K < 20 and for K > 20, where it is clear how the closed-loop system becomes unstable as K increases. Example 8.4.5 Consider the closed-loop system of Figure 8.30. Determine the range of values of K for which the closed-loop system is stable. Solution We have GðsÞFðsÞ ¼
Figure 8.30
K ; s1
K>0
The closed-loop block diagram of Example 8.4.5.
336
Chapter 8
Since the open-loop transfer function is of nonminimum phase (it has a pole in the right-half complex plane), it follows that for the closed-loop system to be stable, the Nyquist diagram of GðsÞFðsÞ, having the opposite direction than that of the Nyquist path N , must encircle N ¼ P ¼ 1 times the critical point ð1; j0Þ (Theorem 8.4.4). We next construct the Nyquist diagram. We have Gð j!ÞFð j!Þ ¼
K j! 1
If we use the results of case 1 of Subsec. 8.4.4, it follows that the Nyquist diagram will be a circle, as in Figure 8.31. Therefore, since the Nyquist diagram must encircle once the critical point ð1; j0Þ in the counterclockwise direction, in order that the closed-loop system be stable, it follows that the circle must have a radius greater than 1/2, i.e., there must be K > 1. Of course, one would arrive at the same results by applying one of the algebraic stability criteria. Indeed, since the denominator of the closed-loop transfer function is given by 1 þ GðsÞFðsÞ ¼ 1 þ
K s1þK ¼ s1 s1
it follows that the characteristic polynomial pc ðsÞ of the closed-loop system is pc ðsÞ ¼ s 1 þ K. Upon using Routh’s criterion, it readily follows that for the closed-loop system to be stable, there must be K > 1. Finally, it is worth mentioning that a careful examination of Figure 8.31 shows that for the present example (where the open-loop transfer function GðsÞFðsÞ is nonminimum phase), even though the closed-loop system is stable (i.e., for K > 1), the phase margin is positive, whereas the gain margin is negative. Example 8.4.6 An automatic control system for controlling the thickness of sheet metal is given in Figure 1.10. A simplified block diagram of this system is given in Figure 8.32. Construct the Nyquist diagram and determine the range of values of K for which
Figure 8.31
Nyquist diagram of Example 8.4.5.
Frequency Domain Analysis
Figure 8.32
337
The block diagram of the automatic thickness control system.
the closed-loop system is stable. The controller transfer function Gc ðsÞ is specified as follows: (a) Gc ðsÞ ¼ K, i.e., the controller is a gain amplifier (b) Gc ðsÞ ¼ Kð0:5 þ sÞ, i.e., when the controller is a PD controller (see Subsec. 9.6.2). Solution (a) For this case (i.e., when Gc ðsÞ ¼ K), the system is unstable for all values of K (see Example 8.4.3). (b) For s 2 ð j0þ ; j1Þ, we have GðsÞFðsÞ ¼ Gc ð j!ÞGh ð j!ÞFðj!Þ ¼
Kðj! þ 0:5Þ ¼ x þ jy !2 ð j! þ 1Þ
where x¼
Kð!2 þ 0:5Þ ; !2 ð!2 þ 1Þ
y¼
0:5K !ð!2 þ 1Þ
The point where the Nyquist diagram intersects the x-axis is when y ¼ 0, i.e., when ! ! 1. The Nyquist diagram has the form of Figure 8.33. From this diagram we conclude that the closed-loop system of Figure 8.32 is stable for all values of K in the interval ð0; þ1Þ. We observe that by adding a zero to the open-loop transfer function, which lies to the right of the pole 1 (namely, by adding the zero 0:5 of the controller), the system becomes stable (see also, Figure 8.21h). 8.4.6
Comparison Between Algebraic Criteria and the Nyquist Criterion
We close Sec. 8.4 by making a comparison between the algebraic criteria of Chap. 6 and the Nyquist criterion. The algebraic criteria have the following characteristics: a.
They require knowledge of the analytical expression of the characteristic polynomial of the system under control b. The computational effort required to apply the algebraic criteria is extremely small
338
Chapter 8
Figure 8.33
c.
Nyquist diagram of Example 8.4.6.
the algebraic criteria do not give any information on the exact position of the poles in the complex plane and, therefore, they do not give any information on the relative stability of the system or its transient response.
The Nyquist criterion has the following characteristics: a.
The Nyquist diagram of GðsÞFðsÞ can be determined experimentally and relatively easily b. The stability is determined by simply inspecting the Nyquist diagram of the open-loop transfer function GðsÞFðsÞ c. The relationship between the Nyquist diagram and the amplifier gain K of the system is well understood, which allows us to take advantage of the influence of the variations of K in order to secure stability of the closedloop system d. The Nyquist diagram gives information on the relative stability and the transient response of the system.
8.5 8.5.1
BODE DIAGRAMS Introduction
Simply speaking, the Bode and the Nyquist diagrams are plots of the transfer function Hð j!Þ as a function of the angular frequency !. The difference between the two diagrams is that the Nyquist diagram consists of only one curve, while the Bode diagrams consist of two curves. The two curves of the Bode diagrams are the amplitude M curve of Hð j!Þ in decibels, i.e., the curve A ¼ 20 log M ¼ 20 log jHð j!Þj and the phase ’ of Hð j!Þ, i.e., the curve ’ ¼ Hð j!Þ. The Bode and Nyquist diagrams essentially offer the same information about the transfer function Hð j!Þ. The reason for introducing the Bode diagrams here is
Frequency Domain Analysis
339
that these diagrams, in comparison with the Nyquist diagrams, can be plotted more easily, a fact that has contributed to the extensive use of Bode diagrams. Both Bode and Nyquist diagrams give information about the stability of the system and its phase and gain margins. They are both rather simple to apply and, for this reason, they have become particularly helpful in control system design. 8.5.2
Bode Diagrams for Various Types of Transfer Function Factors
Usually, the transfer function Hð j!Þ involves factors of the form ð j!Þ
, ð j!T þ 1Þ
, and ½ð j!Þ2 þ 2!n ð j!Þ þ !2n
. Consider the following transfer function: Hð j!Þ ¼
Kð j!T10 þ 1Þðj!T20 þ 1Þ ð j!Þ2 ð j!T1 þ 1Þ½ð j!Þ2 þ 2!n ð j!Þ þ !2n
ð8:5-1Þ
The Bode diagrams of Hð j!Þ are the curves of A and ’, where A ¼ 20 log M ¼ 20 log jHð j!Þj ¼ 20 log
jKjj j!T10 þ 1jj j!T20 þ 1j jð j!Þ jj j!T1 þ 1jjð j!Þ2 þ 2!n ð j!Þ þ !2n j 2
¼ 20 log jKj þ 20 log j j!T10 þ 1j þ 20 log j j!T20 þ 1j 20 log jð j!Þ2 j 20 log j j!T1 þ 1j 20 log jð j!Þ2 þ 2!n ð j!Þ þ !2n j
ð8:5-2Þ
and ’ ¼ Hð j!Þ ¼
Kþ
j!T10 þ 1 þ
j!T20 þ 1
ð j!Þ2
j!T1 þ 1
ð j!Þ2 þ 2!n ð j!Þ þ !2n ð8:5-3Þ
From Eqs (8.5-2) and (8.5-3) we observe that plotting the curves of A and ’ becomes particularly easy because curve A is actually the sum of the curves of the individual terms 20 log jKj, 20 log j j!T10 þ 1j; . . . ; etc., and curve ’ is actually the sum of the curves of the individual terms K, j!T10 þ 1; . . . ; etc. In the sequel we will show that the sketching of each of these individual terms is rather simple. 1 The Constant Term K In this case we have A ¼ 20 log jKj 08; when K > 0 ’¼ 1808; when K > 0 The plots of A and ’ are given in Figure 8.34.
ð8:5-4aÞ ð8:5-4bÞ
340
Chapter 8
Figure 8.34
The plots of (a) amplitude A and (b) phase ’ when HðsÞ ¼ K.
2 Poles or Zeros at the Origin: ð j!Þ In this case we have A ¼ 20 log jð j!Þ
j ¼ 20 log !
ð8:5-5aÞ
ð j!Þ
¼ 908
ð8:5-5bÞ
’¼
Relation (8.5-5a) presents a family of lines on semilogarithmic paper. All these lines meet at the point where A ¼ 0 and ! ¼ 1. Their slopes are 20 , in which case we say that these slopes are 20 dB/decade. This means that if the frequency is increased from ! 0 to 10! 0 , the change in amplitude is 20 dB. Indeed, from relation (8.5-5a), we have A ¼ Að10! 0 Þ Að! 0 Þ ¼ 20 log jð j10! 0 Þ
j 20 log jð j! 0 Þ
j ¼ 20 log ! 0 20 log 10 ð 20 log ! 0 Þ ¼ 20 The plots of A and ’ are given in Figure 8.35.
Frequency Domain Analysis
Figure 8.35
341
The plots of (a) amplitude A and (b) phase ’ when HðsÞ ¼ s
.
3 Poles or Zeros of the Form ð j!T þ 1Þ In this case we have pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi A ¼ 20 log jðj!T þ 1Þj ¼ 20 log !2 T 2 þ 1
ð8:5-6aÞ
’ ¼
tan1 ð!TÞ
ð8:5-6bÞ
Relation (8.5-6a) is a family of curves which may be plotted approximately using the following: a. When ! 1=T, Eq. (8.5-6a) becomes pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi A ¼ 20 log !2 T 2 þ 1 ’ 20 log 1 ¼ 0
ð8:5-7aÞ
b. When ! ¼ 1=T, Eq. (8.5-6a) becomes pffiffiffi A ¼ 20 log 2 ’ 3
ð8:5-7bÞ
c. When ! 1=T, Eq. (8.5-6a) becomes A ’ 20 log !T
ð8:5-7cÞ
342
Chapter 8
The frequency ! ¼ 1=T is called the corner frequency. Hence, the plot of Eq. (8.5-6a) consists, approximately, of two asymptotes. The first asymptote coincides with the 0 dB axis and holds for 0 ! 1=T. The other asymptote crosses over the !-axis at the point ! ¼ 1=T, has a slope of 20 dB and holds for 1=T ! þ1. At the corner frequency ! ¼ 1=T, the plot of A, according to relation (8.5-7b), is approximately equal to 3 dB. The plot of phase ’, given by Eq. (8.5-6b), is sketched by assigning several values to ! and calculating the respective values of the phase ’. Some characteristic values of ’ are the following: a. When ! ¼ 0, then ’ ¼ 08 b. When ! ¼ 1=T, then ’ ¼ 458 c.
When ! ¼ 1, then ’ ¼ 908 .
Thus, the plot of ’ starts from the point zero, passes through the point 458 , and terminates asymptotically to the line 908 . The plots of A and ’, when ¼ 1, are given in Figure 8.36. 2 1 Factors of the Form ½!2 n ð j!Þ þ 2!n ð j!Þ þ 1
In this case, we have
Figure 8.36
The plots of (a) amplitude A and (b) phase ’ when HðsÞ ¼ ðTs þ 1Þ
.
Frequency Domain Analysis
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi A ¼ 20 log ð1 u2 Þ2 þ 42 u2 2u ’ ¼
tan1 1 u2
343
ð8:5-8aÞ ð8:5-8bÞ
where u ¼ !=!n . The plot of Eq. (8.5-8a) is sketched approximately using the following: a. When u 1, then A ’ 20 log 1 ¼ 0 b. When u 1, then A ’ 40 log u c. When ¼ 1, then A ¼ 20 log j1 þ u2 j d. When ¼ 0, then A ¼ 20 log j1 u2 j. Thus, the curve of Eq. (8.5-8a) consists, approximately, of two asymptotes. The first asymptote coincides with the 0 dB-axis and the second has a slope of 40 dB and crosses over the u-axis at the point u ¼ 1. In the vicinity of the point of intersection of the two asymptotes, the form of the curve of A is decisively influenced by the damping ratio . The plot of the phase ’, which is given by Eq. (8.5-8b), has the following characteristics: a. When u ¼ 0, then ’ ¼ 08 b. When u ¼ 1, then ’ ¼ 908 c. When u ¼ 1, then ’ ¼ 1808 . The plots of A and ’, when ¼ 1 (which is the most common case), are given in Figure 8.37. 8.5.3
Transfer Function Bode Diagrams
The plots of the amplitude A and the phase ’ of a transfer function HðsÞ start by plotting each factor in A and ’, separately. Subsequently, we add the curves of all factors in A and the curves of all factors in ’, resulting in the curves A and ’ sought. This methodology is presented, step by step, in the following example. Example 8.5.1 Consider the transfer function Hð j!Þ ¼
10ð j! þ 1Þ ð j!Þð101 j! þ 1Þð3 103 j! þ 1Þ
Plot the Bode diagrams A and ’ of Hð j!Þ. Solution With respect to the diagram A, define A1 ¼ 20 log 10 ¼ 20, A2 ¼ 20 log j j! þ 1j, A3 ¼ 20 log j j!j, A4 ¼ 20 log j101 j! þ 1j, and A5 ¼ 20 log j3 103 j! þ 1j. Then, the diagram of the amplitude A is A ¼ A1 þ A2 þ A3 þ A4 þ A5 . Thus, in order to plot the curve A, it suffices to plot each curve A1 , A2 , A3 , A4 , and A5 separately and then add them. To this end, plot the curves A1 , A2 , A3 , A4 , and A5 by applying the results of Subsec. 8.5.2. The break points of A2 , A4 , and A5 are 1, 10, and 103 =3, respectively. The plots of A1 , A2 , A3 , A4 , and A5 , as well as that of A, are given in Figure 8.38.
344
Figure 8.37
Chapter 8
The plots 2 1 1 ½!2 n s þ 2!n s þ 1 .
Figure 8.38
of
(a)
amplitude
A
and
(b)
phase
’
when
HðsÞ ¼
The plot of the amplitude A of the transfer function of Example 8.5.1.
Frequency Domain Analysis
345
With respect to the diagram of ’, define ’1 ¼ 10 ¼ 0, ’2 ¼ j! þ 1 ¼ tan1 !, ’3 ¼ j! ¼ 908, ’4 ¼ 101 j! þ 1 ¼ tan1 101 !, and ’5 ¼ 3 103 j! þ 1 ¼ tan1 3 103 !. The total phase ’ ¼ ’1 þ ’2 þ ’3 þ ’4 þ ’5 is determined following the same steps as for determining A. The plots of ’1 , ’2 , ’3 , ’4 , and ’5 , as well as that of ’, are given in Figure 8.39. 8.5.4
Gain and Phase Margin
Consider the definitions for gain and phase margin given in Eqs (8.4-21) and (8.4-22), respectively. These two definitions are given on the basis of the Nyquist diagram of the open-loop transfer function and are shown in Figure 8.25. Similarly, the gain and phase margins can be defined on the basis of the Bode diagrams. To this end, consider Figure 8.40. Here, the Bode diagrams A and ’ of a certain open-loop transfer function Gð j!ÞFð j!Þ are depicted. In this figure, when ! ¼ ! 0 , then jGð j! 0 Þ Fð j! 0 Þj ¼ 1 and hence Að! 0 Þ ¼ 0, and when ! ¼ !c , then ’ð!c Þ ¼ 1808. Thus, Kg is the vertical straight line which connects the point !c with the curve A, while ’p is the vertical straight line which connects the point ! 0 with the curve ’. More specifically, from Figure 8.40, we have Kg ¼ 20 log jGð j!ÞFð j!c Þj ¼ DE dB ’p ¼ 1808 þ
Gð j! 0 ÞFð j! 0 Þ ¼ 1808 þ ½1808 þ ðCBÞ8 ¼ ðCBÞ8
ð8:5-9Þ ð8:5-10Þ
In Figure 8.40, both margins Kg and ’p are positive. Therefore, for this particular open-loop system, the closed-loop system is stable. Example 8.5.2 This example refers to the system of automatically adjusting the opening and closing of the pupil of the human eye. A simplified diagram of this system is given in Figure
Figure 8.39
The plot of the phase ’ of the transfer function of Example 8.5.1.
346
Chapter 8
Figure 8.40
Definitions of gain margin and phase margin in Bode diagrams.
8.41. Here, the constant a, which is the inverse of the time constant of the pupil of the eye, is usually 0.5 sec; the constant K is the gain constant of the pupil; and FðsÞ is the feedback transfer function of the signal from the optic nerve, where T is the time constant, which is usually 0.2 sec. For simplicity, assume that T is zero. Using the Bode diagrams, determine the range of values of K for which the closed-loop system is stable. Solution The open-loop transfer function has the form Gð j!ÞFð j!Þ ¼
Figure 8.41
K 8K ¼ ; ð j! þ 0:5Þ3 ð2j! þ 1Þ3
K >0
Closed-loop system for the automatic control of human vision.
Frequency Domain Analysis
347
To plot the diagram of p the amplitude A, define A1 ¼ 20 logð8KÞ and A2 ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 60 log j2j! þ 1j ¼ 60 log 4!2 þ 1. The plot of A2 has a break point at ! ¼ 1=2 ¼ 0:5 rad/sec, and is shown in Figure 8.42. The plot of A1 is a horizontal line which passes through the point 20 logð8KÞ. The diagram of the amplitude A is the sum of A1 and A2 , which means that the diagram A essentially is the plot A2 moving upwards or downwards depending on the sign of A1 . To plot the diagram of the phase ’, define ’1 ¼ 8K ¼ 08 and ’2 ¼ 3 tan1 ð2!Þ. The diagram of ’ is the sum of ’1 and ’2 , shown in Figure 8.43. The critical frequency !c for which the total phase becomes 1808 satisfies the following equation ’ð!c Þ ¼ 1808
or pffiffiffi 2!c ¼ tan 608 ¼ 3
3 tan1 ð2!c Þ ¼ 1808
or
Hence, the critical frequency is !c ¼ 0:87 rad/sec. The gain margin Kg is given by qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Kg ¼ 20 log jGð j!c ÞFð j!c Þj ¼ 20 logð8KÞ þ 60 log 4!2c þ 1 ¼ 20 logð8KÞ þ 18 For the closed-loop system to be stable, the gain margin Kg and the phase margin ’p must both be positive. For Kg to be positive, we must have 20 logð8KÞ < 18 or K < 1. For values of K less than the total amplitude diagram of Gð j!ÞFð j!Þ will cross over the 0 dB axis at a frequency whose value is less than !c . From Figure 8.43 we conclude that for ! < !c the phase ’ is less (in absolute value) than 1808, and hence ’p > 0. Therefore, for the closed-loop system to be stable it must hold that 0 < K < 1.
Figure 8.42 human vision.
The amplitude diagram of a closed-loop system for the automatic control of
348
Chapter 8
Figure 8.43
The phase diagram of the closed-loop system for the automatic control of
human vision.
8.5.5
Bode’s Amplitude–Phase Theorem
One of the most important contributions of Bode is the celebrated Bode’s amplitude–phase theorem. According to this theorem, for every minimum phase system (e.g., a system without poles or zeros in the right complex plane), the phase Gð j!ÞFð j!Þ of the open-loop transfer function Gð j!ÞFð j!Þ of the system is related to its amplitude jGð j!ÞFð j!Þj in a unique manner. The exact expression is ð 1 þ1 dM Gð j! 0 ÞFð j! 0 Þ ¼ WðuÞdu (in radians) 1 du where M ¼ ln jGð j!ÞFð j!Þj;
u ¼ ln
! !0
and
WðuÞ ¼ lnðcoth juj=2Þ
Here, ! 0 is the critical amplitude frequency, where jGð j! 0 ÞFð j! 0 Þj ¼ 1. The hyperbolic cotangent is defined as coth x ¼ ðex þ ex Þ=ðex ex Þ. The weighting function WðuÞ is presented in Figure 8.44. From its form we conclude that the phase of Gð j!ÞFð j!Þ depends mainly upon the gradient dM=du at the frequency ! 0 and to a lesser degree upon the gradient dM=du at neighboring frequencies. If we approximate WðuÞ with an impulse function at the point ! 0 and assume that the slope of Gð j!ÞFð j!Þ remains constant and is equal to 20n dB/ decade for a band of frequencies of about 1 decade above and one below the frequency ! 0 , then we can arrive at the approximate relation Gð j! 0 ÞFð j! 0 Þ ffi n 908. We know that in order to have positive phase margin (stability), Gð j! 0 ÞFð j! 0 Þ > 1808 at the frequency ! 0 , where jGð j! 0 ÞFð j! 0 Þj ¼ 1. For this reason, if ! 0 is the desired critical amplitude frequency, it is plausible that the gradient of jGð j!ÞFð j!Þj is 20 dB/decade ðn ¼ 1Þ for 1 decade above and 1 decade below the critical amplitude frequency ! 0 . Then, according to the approximate
Frequency Domain Analysis
Figure 8.44
349
The weighting function WðuÞ ¼ lnðcoth juj=2Þ.
relation Gð j! 0 ÞFð j!Þ ffi 908, the phase margin is about 908. To obtain a more desired (smaller) value for the phase margin, it suffices that the gradient of the logarithmic amplitude curve of Gð j!ÞFð j!Þ is equal to 20 dB/decade for a band of frequencies of 1 decade with center frequency the desired critical frequency ! 0 . Example 8.5.3 Using Bode’s theorem, design a satisfactory controller for the altitude control of a spaceship, which is described by the transfer function Gs ðsÞ ¼ 0:9=s2 . We wish to obtain a satisfactory damping ratio and a bandwidth of about 0.2 rad/sec. Solution The block diagram of the compensated closed-loop system is shown in Figure 8.45. The amplitude diagram of the uncompensated system, with transfer function Gs ðsÞ ¼ 0:9=s2 , is shown in Figure 8.46, from which it follows that the slope is constant and equal to 40 dB/decade, due to the double pole at s ¼ 0. According
Figure 8.45
The block diagram of the spaceship altitude control system.
350
Chapter 8
Figure 8.46
Bode amplitude diagrams for 0:9=s2 and 0:9ð1 þ 20sÞ=s2 .
to the design requirements, we should obtain a constant slope of 20 dB/decade for a decade of frequencies near the desired amplitude frequency. Thus, it is obvious that the controller must add, in a band of frequencies near the desired critical amplitude frequency, a slope of þ20 dB/decade. This means that the controller Gc ðsÞ must be of the form Gc ðsÞ ¼ KðTs þ 1Þ, which is a PD controller (see Subsec. 9.6.2). This controller adds a zero, which must yield a gradient 20 dB/ decade near the critical amplitude frequency, as well as an amplification K, which must yield the desired bandwidth. At first, we assume that the criical frequency and the bandwidth are the same for the system, a fact which will be checked later. Since we wish a bandwidth (and hence a critical amplitude frequency) of 0.2 rad/sec, we choose the corner frequency 1=T to be four times lower than the desired critical frequency, e.g., we choose T ¼ 20. This is done in order to keep the slope 20 dB/ decade for frequencies lower than 0.2 rad/sec. Hence, the open-loop transfer function has the form
0:9 GðsÞFðsÞ ¼ Gc ðsÞGs ðsÞ ¼ ð20s þ 1Þ 2 s
The amplitude curve of GðsÞFðsÞ is given in Figure 8.46. From this figure, it follows that the gradient is 20 dB/decade in the band of frequencies from 0.1 to 1 rad/sec (one decade of frequencies near the desired critical frequency 0.2 rad/sec). However, for ! ¼ 0:2 rad/sec, the amplitude of GðsÞFðsÞ is 0:9ð1 þ 20j!Þ 20 log ffi 39:3 dB ð j!Þ2 !¼0:2
Frequency Domain Analysis
Figure 8.47
351
Frequency response of the closed-loop system.
Thus, if we choose 20 log K ¼ 39:3 dB, which yields K ¼ 0:0108, it follows that the frequency ! ¼ 0:2 rad/sec is the critical frequency of the compensated system. At this point, our design is completed. Checking the results, from Figure 8.47 it follows that it is true that the critical frequency and the bandwidth are the same. If we further draw the phase curve of the open-loop system transfer function GðsÞFðsÞ, we will have that the phase margin is 758, which is quite satisfactory.
8.6 8.6.1
NICHOLS DIAGRAMS Consant Amplitude Loci
Consider a closed-loop system with unity feedback transfer function, i.e., with FðsÞ ¼ 1. The transfer function of the closed-loop system is given by HðsÞ ¼
GðsÞ 1 þ GðsÞ
ð8:6-1Þ
In the sinusoidal steady state, expression (8.6-1) becomes Hð j!Þ ¼
Gð j!Þ ¼ jHð j!Þj 1 þ Gð j!Þ
Hð j!Þ ¼ M
’
ð8:6-2Þ
Let Gð j!Þ ¼ Re Gð j!Þ þ jIm Gð j!Þ ¼ x þ jy Then, the amplitude M may be written as pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi x2 þ y2 jGð j!Þj ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi M¼ j1 þ Gð j!Þj ð1 þ xÞ2 þ y2
ð8:6-3Þ
ð8:6-4Þ
Equation (8.6-4) is the mathematical expression which defines the locus of constant amplitude M in the Gð j!Þ-plane. This locus is the circumference of a circle. Indeed, Eq. (8.6-4) can be written in the well-known form of a circle, as follows
352
Chapter 8
"
M2 x 1 M2
#2
M þy ¼ 1 M2
2 ð8:6-5Þ
2
Consequently, for any consant M, relation (8.6-5) represents the circumference of a circle with at center the point ðxc ; yc Þ and radius R, where M2 xc ¼ ; 1 M2
yc ¼ 0;
and
M R¼ 1 M2
For the particular value of M ¼ 1, Eq. (8.6-5) is not defined. However, from Eq. (8.6-4) we can obtain that for M ¼ 1, the constant M locus is the straight line. x¼
1 2
ð8:6-6Þ
Typical curves of the loci (8.6-5) and (8.6-6) are given in Figure 8.48, where we can observe that the circles of constant amplitude M are symmetrical to the lines y ¼ 0 and x ¼ 1=2. To the left of the line x ¼ 1=2 are the circles with M > 1 and to the right are the circles with M < 1. At the boundary values, i.e., when M ! 1 and M ! 0, the radii tend to zero, which means that the circles degenerate to the points ð1; 0Þ and ð0; 0Þ, respectively. The above results are very useful for correlating the curve of the open-loop transfer function Gð j!Þ in the Gð j!)-plane with the curve of the amplitude Mð!Þ of Hð j!Þ. This correlation is given in Figure 8.49, where it is shown that the tangent point of the two curves is also the resonant point, which means that the curve Mð!Þ reaches its maximum value Mp for the frequency for which the curve Gð j!Þ is tangent to the circumference of the circle with constant M and amplitude equal to Mp .
Figure 8.48
The loci of constant amplitude M.
Frequency Domain Analysis
353
Figure 8.49 The correlation between the amplitude M curves and the corresponding Gð j!Þ curves. (a) Constant amplitude M circles and the curves of three open-loop transfer functions; (b) amplitude curves of three open-loop transfer functions.
8.6.2
Constant Phase Loci
Using relation (8.6-3), the phase ’ of Hð j!Þ may be written as hyi y tan1 ’ ¼ Hð j!Þ ¼ tan1 x 1þx Next, take the tangent of both sides of Eq. (8.6-7) to yield hyi y y tan ’ ¼ tan tan1 tan1 ¼ 2 x 1þx x þ x þ y2 If we set N ¼ tan ’ in Eq. (8.6-8), we have
ð8:6-7Þ
ð8:6-8Þ
354
Chapter 8
x2 þ x þ y2
y ¼0 N
The above relation may be written as 1 2 1 2 N2 þ 1 þ y ¼ xþ 2 2N 4N 2
ð8:6-9Þ
Equation (8.6-9) represents a circle with center at the point ðxc ; yc Þ and radius R, where sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 1 N2 þ 1 xc ¼ ; yc ¼ ; and R¼ 2 2N 4N 2 Figure 8.50 shows the circles of Eq. (8.6-9). As in the case of Figure 8.48, the circles of constant phase N ¼ tan ’ are symmetrical to the lines y ¼ 0 and x ¼ 1=2. 8.6.3
Constant Amplitude and Phase Loci: Nichols Charts
In Subsec. 8.6.1 and 8.6.2 we studied the constant amplitude and phase curves, respectively, of a closed-loop system with unity feedback in the Gð j!Þ-plane. The Nichols diagrams, which are presented in the sequel, are curves with the following coordinates: the y-axis is the amplitude jGð j!Þj, in dB, and the x-axis is the phase ¼ Gð j!Þ, in degrees.
Figure 8.50
The loci of constant phase ’.
Frequency Domain Analysis
355
1 Curves with Constant Amplitude M From Relation (8.6-2), we have M¼
jGð j!Þj ; j1 þ jGð j!Þje j j
¼
Gð j!Þ
or M2 ¼
jGð j!Þj2 1 þ 2jGð j!Þj cos þ jGð j!Þj2
The above relation may also be written as " # 2M 2 2M 2 2 jGð j!Þj cos þ ¼0 jGð j!Þj þ M2 1 M2 1
ð8:6-10Þ
For every value of the amplitude M, relation (8.6-10) is a curve whose coordinates are jGð j!Þj (in dB) and (in degrees), as shown in Figure 8.51. These curves are called curves of constant M of the closed-loop system. 2 Curves with Constant Phase ’ From relation (8.6-2), we have ’¼
Gð j!Þ
¼ tan1
Figure 8.51
1 þ Gð j!Þ ¼
Gð j!Þ
1 þ jGð j!Þj cos þ jjGð j!Þj sin
jGð j!Þj sin ¼ 1 þ jGð j!Þj cos
Nichols charts: curves of constant amplitude M and phase ’.
356
Chapter 8
Examining the relation N ¼ tan ’, we have jGð j!Þj sin tan tan 1 þ jGð j!Þj cos ¼ N ¼ tan ’ ¼ tanð Þ ¼ jGð j!Þj sin tan 1 þ tan tan 1þ 1 þ jGð j!Þj cos sin ½1 þ jGð j!Þj cos jGð j!Þj sin cos sin ¼ ¼ 2 2 cos þ jGð j!Þj cos þ jGð j!Þj cos þ jGð j!Þj sin tan
Finally, we arrive at the relation jGð j!Þj þ cos
1 sin ¼ 0 N
ð8:6-11Þ
For every value of the phase ’, relation (8.6-11) is a curve whose coordinates are jGð j!Þj (in dB) and (in degrees), as shown in Figure 8.51. These curves are called curves of constant N (or ’) of the closed-loop system. 3 Closed-Loop System Response Curves The curves of constant M and ’ of Figure 8.51 are essentially the same curves as those of constant M and ’ of Figures 8.48 and 8.50, except for the fact that Figure 8.51 has coordinates the amplitude jGð j!Þj and phase of Gð j!Þ, while Figures 8.48 and 8.50 have coordinates Re Gð j!Þ and Im Gð j!Þ. The basic advantage of Nichols charts, i.e., of the diagrams of Figure 8.51, is that for every change of the gain constant K, the response curve of the closed-loop system moves upwards or downwards without affecting the shape of the response curve. The gain margin Kg and phase margin ’p in Nichols charts are defined in Figure 8.52. In this figure both Kg and ’p are positive. A comparison among Nyquist, Bode, and Nichols diagrams, as far as the gain and phase margins are concerned, is given in Figure 8.53. Finally, an example, where there is correlation between Nichols charts and the response curve of M and ’ is given in Figure 8.54. In particular, in Figure 8.54a the Nichols curve of a transfer function Gð j!Þ is given. This curve is plotted by calculating the value for each coordinate jGð j!Þj in dB and ¼ Gð j!Þ for different values of the frequency
Figure 8.52
The gain margin Kg and the phase margin ’p .
Frequency Domain Analysis
357
Figure 8.53 Comparison among (a) Nyquist, (b) Bode, and (c) Nicholas diagrams for the gain and phase margins.
358
Chapter 8
Figure 8.54 Curves of constant amplitude M and constant phase ’. (a) The Nichols curve of the transfer function Gð j!Þ; (b) the curves M and ’ of the transfer function of the closedloop system. !. In Figure 8.54b, the Bode curves of a closed-loop system are given. These curves are plotted as follows. For each value of the frequency !, the Nichols crosses over the curves of M and ’. By using several values of !, we find the respective values of M and ’ in Figure 8.54a and, subsequently, we transfer these values to Figure 8.54b. By joining these different values of M and ’, we obtain the curves of Figure 8.54b. Remark 8.6.1 The Nyquist and Nichols diagrams of Gð j!Þ have the common characteristic in that they both consist of only one curve, with the frequency ! as a free parameter.
Frequency Domain Analysis
359
However, they differ in that the Nyquist diagrams have coordinates Re Gð j!Þ and Im Gð j!Þ, while the Nichols diagrams have coordinates jGð j!Þ and Gð j!Þ. On the contrary, Bode diagrams consists of two separate curves: the amplitude curve and the phase curve. Both of these curves are functions of the frequency !. In Figure 8.55 we present, for some typical transfer functions, the root loci and the Nyquist diagrams. These diagrams are worth studying because they facilitate the comparison of the basic concepts developed in Chaps 7 and 8, in relation to the very popular classical control design tools in the frequency domain: namely, the root
Figure 8.55
Root loci and Nyquist diagrams for typical transfer functions (continued).
360
Figure 8.55
Chapter 8
(contd.)
locus and the Nyquist diagrams (the Bode diagrams may readily be derived from the Nyquist diagrams). Remark 8.6.2 For simplicity, in the presentation of Nichols charts it was assumed that FðsÞ ¼ 1, in which case the open-loop transfer function is GðsÞ. When FðsÞ 6¼ 1, we have the more general case where
Frequency Domain Analysis
Figure 8.55
361
(contd.)
GðsÞ 1 GðsÞFðsÞ 1 G ðsÞ H ðsÞ ¼ HðsÞ ¼ ¼ ¼ 1 þ GðsÞFðsÞ FðsÞ 1 þ GðsÞFðsÞ FðsÞ 1 þ G ðsÞ FðsÞ From the above relation we conclude that when FðsÞ 6¼ 1, we can directly apply the results of the present section for H ðsÞ, as long as, instead of GðsÞ, we set G ðsÞ ¼ GðsÞFðsÞ. To determine HðsÞ ¼ H ðsÞF 1 ðsÞ, we must multiply the two transfer functions H ðsÞ and 1=FðsÞ. This can be done easily by using Bode diagrams.
362
Chapter 8
PROBLEMS 1. Plot the Nyquist diagrams and investigate the stability of the closed-loop systems for the following open-loop transfer functions: (a)
GðsÞFðsÞ ¼
Kðs 1Þ sðs þ 1Þ
(d)
GðsÞFðsÞ ¼
Kðs þ 3Þ sð1 þ sÞð1 þ 2sÞ
(b)
GðsÞFðsÞ ¼
10Kðs þ 2Þ s3 þ 3s2 þ 10
(e)
GðsÞFðsÞ ¼
Kðs 3Þ sð1 þ sÞð1 þ 2sÞ
(c)
GðsÞFðsÞ ¼
Ks 1 0:5s
(f)
GðsÞFðsÞ ¼
s2 ð1
K þ sÞð1 þ 2sÞ
2. Find the gain and phase margins of the systems of Problem 1. 3. The block diagram of a laser beam control system used for metal processing is shown in Figure 8.56. Plot the Nyquist diagram for K > 0 and investigate the stability of the system. 4. The block diagram of a position control system for a space robot arm is given in Figure 8.57. Determine the stability of the system using the Nyquist diagram, for K > 0. 5. Let GðsÞFðsÞ ¼ KðTs þ 1Þ=s2 . Plot the Nyquist diagram and determine the value of T so that the phase margin is 458. 6. Consider a field-controlled DC motor represented by the block diagram in Figure 8.58. Draw the Nyquist diagram. Furthermore: (a) Determine the gain K so that the gain margin is 20 dB (b) Determine the value of K so that the phase margin is 608. 7. Plot the Bode diagrams and determine the gain and phase margins of the systems having the following open-loop transfer functions: (a)
GðsÞFðsÞ ¼
sþ1 0:1s þ 1
(d)
GðsÞFðsÞ ¼
(b)
GðsÞFðsÞ ¼
10 s ðs þ 1Þ
(e)
GðsÞFðsÞ ¼
100 s ðs þ 2s þ 1Þ
(c)
GðsÞFðsÞ ¼
sþ1 sð0:1s þ 1Þ
(f)
GðsÞFðsÞ ¼
ðs þ 1Þ2 sð0:1s þ 1Þ3 ð0:01s þ 1Þ
Figure 8.56
2
s2 ðs 2
0:1s þ 1 þ 1Þð0:2s þ 1Þ2 2
Frequency Domain Analysis
363
Figure 8.57
8. The block diagram of the orientation control system of a space telescope is shown in Figure 8.59. Determine the value of the gain K for which the phase margin is 508. Find the gain margin for this case. 9. Consider the field-controlled DC motor system of Problem 6. For K ¼ 4, plot the Bode diagram of the system, find the phase-crossover and gain-crossover frequencies and determine the gain margin and the phase margin. Is the system stable? (b) Determine the value of K for which the phase margin is 508. (c) Find the value of K so that the gain margin is 16 dB.
(a)
10. The Bode diagram of a system is given in Figure 8.60. Determine the transfer function of the system. 11. Determine the transfer function GðsÞ of a system, based on the measurement data shown in Table 8.1. 12. For K ¼ 1, plot the Nichols diagram for the unity feedback systems having (a)
GðsÞ ¼
Kðs þ 1Þ ; sð0:1s þ 1Þð0:01s þ 1Þ
ðbÞ
GðsÞ ¼
K þ 1Þ
s2 ðs
Plot the response of the closed-loop systems and find the values of K for which Mp ¼ 1:3:
Figure 8.58
Figure 8.59
Figure 8.60 Table 8.1 !
jGð j!Þj
’8
0.1000 0.2154 0.4642 1.0000 2.1544 4.6416 10.0000 21.5443 46.4159 100.0000
0.0481 0.0481 0.0482 0.0485 0.0501 0.0592 0.1244 0.0135 0.0024 0.0005
0:2204 0:4750 1:0249 2:2240 4:9571 12:6895 84:2894 166:5435 174:8261 177:6853
Frequency Domain Analysis
365
Figure 8.61
13. Consider the open-loop transfer function GðsÞFðsÞ ¼
Kð0:25s2 þ 0:5s þ 1Þ sð1 þ 2sÞ2 ð1 þ 0:25sÞ
The Nichols diagram is shown in Figure 8.61, for K ¼ 1. Determine the gain K so that the gain margin is at least 10 dB and the phase margin is at least 458. 14. The Nichols chart of a system is shown in Figure 8.62. Using the data given below determine: (a) the resonance peak Mp in dB, (b) the resonant frequency, (c) the bandwidth, (d) the phase margin, and (e) the gain margin of the system. Angular frequency
!1
!2
!3
!4
rad/sec
1
3
6
10
15. For the control system shown in Figure 8.63, plot the Nyquist, Bode, and Nichols diagrams, the constant M and ’ loci, and the curves Mð!Þ and ’ð!Þ, for K ¼ 1, 10, and 100. Comment on the results by comparing the diagrams.
366
Chapter 8
Figure 8.62
Figure 8.63
BIBLIOGRAPHY Books 1. 2. 3. 4. 5. 6.
JJ D’Azzo, CH Houpis. Linear Control System Analysis and Design, Conventional and Modern. New York: McGraw-Hill, 1975. JJ DiStefano III, AR Stubberud, IJ Williams. Feedback and Control Systems. Schaum’s Outline Series. New York: McGraw-Hill, 1967. RC Dorf, RE Bishop. Modern Control Analysis. London: Addison-Wesley, 1995. GF Franklin, JD Powell, A Emami-Naeini. Feedback Control of Dynamic Systems. Reading, MA: Addison-Wesley, 1986. NS Nise. Control Systems Engineering. New York: Benjamin and Cummings, 1995. K Ogata. Modern Control Systems. London: Prentice Hall, 1997.
9 Classical Control Design Methods
9.1
GENERAL ASPECTS OF THE CLOSED-LOOP CONTROL DESIGN PROBLEM
The problem of designing an open- or closed-loop control system can be stated using Figure 9.1 as follows (see also Subsec. 1.3): given the system G under control and its desirable behavior yðtÞ, find an appropriate controller Gc such that the composite system (i.e., the system composed of the controller Gc plus the system G) yields the desired output yðtÞ. A general form of the controller Gc in closed-loop systems is given in Figure 9.2, where G1 and G2 are the controllers in ‘‘series’’ and F is the controller in ‘‘parallel’’ or the feedback loop controller. In practice, in most case, various combinations of G1 , G2 , and F are used, as for example G2 alone, F alone, G1 and F in pair, G2 and F in pair, etc. For the design of the controller Gc , many methods have been developed, which may be distinguished in two categories: the classical and the modern. The classical methods are based mainly on the root locus techniques and the Nyquist, Bode, and Nichols diagrams. These methods are graphical and they are developed in the frequency domain. The advantage of the classical methods is that they are rather simple to apply. However, they have certain disadvantages. One disadvantage is that classical methods can be applied to SISO systems. In recent years, major efforts have been made to extend many of the SISO classical methods to MIMO systems. Another disadvantage is that in many cases, due to their graphical nature, these methods do not give the necessary and sufficient conditions which must be satisfied for the design problem to have a solution. This means that in situations where the design requirements cannot be satisfied, the designer will be searching in vain for the solutions of the problem. In contrast to classical methods, modern control design methods can be characterized as analytical and are mostly developed in the time domain. Necessary and sufficient conditions are established for the design problem to have a solution. Many of these methods are based upon the idea of minimizing a cost function (or performance index). In particular, one of the major problems of modern control theory can be formulated on the basis of Figure 9.3, as follows: we are given the system G whose 367
368
Chapter 9
Figure 9.1
Block diagrams of (a) open- and (b) closed-loop control systems.
Figure 9.2
Typical structure of a closed-loop system.
Figure 9.3
Block diagram of a closed-loop system with a reference model.
Classical Control Design Methods
369
behavior is considered unsatisfactory. We are also given a second system, system M, whose behavior is considered ideal. This system M is called the reference model or simply the model. Find an appropriate controller Gc (i.e., find G1 , G2 , and F) which minimizes a specific cost function. The cost function is designated by the letter J and its typical form is given by ð 1 T T J ¼ lim e ðtÞeðt dt ð9:1-1Þ T!1 T 0 where eðtÞ ¼ yðtÞ ym ðtÞ is the error between the desired behavior (output) ym ðtÞ of the reference model and the actual behavior (output) yðtÞ of the given system. It is clear that the solution of Eq. (9.1-1) is the optimal solution to the problem. The field of modern control engineering which is based upon the minimization of cost functions is called optimal control. Chapter 11 constitutes an extensive introduction to this very important approach of optimal control. To give a simple comparison between classical and optimal design methods, the following example is presented. Example 9.1.1. Consider the closed-loop system of Figure 9.4. Let rðtÞ ¼ 1. Find the appropriate value of the parameter K which minimizes the cost function ð1 jeðtÞj dt ð9:1-2Þ J¼ 0
Solution To facilitate the understanding of the main idea of optimal control, no strict mathematical proof will be given here, but rather a simple and practical explanation of the solution of the problem. In Figure 9.5a the output yðtÞ is given for various values of K. In Figures 9.5b and 9.5c the waveforms of jeðtÞj and JðKÞ are given, where JðKÞ is the cost function (9.1-2) with the amplification constant K as a parameter. From these figures we conclude that the optimal control approach guarantees the optimal solution K3 . If a classical method were applied, the resulting solution for K would be, in general, different from the optimal solution K3 . This chapter is devoted to the classical control design methods. In particular, we will present control design techniques using proportional controllers and PID controllers, i.e., controllers consisting of three terms: P(proportional), I(integral), and D(derivative). Also, use of special types of circuits, such as phase-lead, phase-
Figure 9.4
A closed-loop system with a proportional controller.
370
Figure 9.5
Chapter 9
Waveforms of (a) yðtÞ, (b) jeðtÞj, and (c) JðKÞ of Example 9.1.1.
lag, and phase lag-lead circuits will be used for closed-loop system compensation. At the end of the chapter we give a brief description of certain quite useful classical methods of optimal cotnrol, which preceded the modern advanced techniques of optimal control presented in Chap. 11. Chapters 10 and 11 give an introduction to modern state-space control techniques. Specifically, in Chap. 10 the following very important algebraic control design techniques are presented: eigenvalue assignment, input–output decoupling, exact model-matching, and state observers. Chapter 11 gives an introduction to optimal control covering the well-known problems of optimal regulator and optimal servomechanism. Further material on even more recent results on modern control design techniques are presented in the remaining chapters (Chaps 12–16).
Classical Control Design Methods
9.2
371
GENERAL REMARKS ON CLASSICAL CONTROL DESIGN METHODS
As already mentioned in Sec. 9.1, the classical control design methods are mainly graphical and as a result they are mostly based upon experience. A typical example of system design with classical control methods is the closed-loop system of Figure 9.6a. Assume that the performance of the given system GðsÞ is not satisfactory, e.g., assume that the output yðtÞ is slower than expected. To improve its performance, we introduce the controller Gc ðsÞ. Suppose that the transfer function Gð j!Þ of the system under control is tangent to the circumference of the circle of constant M at the frequency !1 (Figure 9.6b). To improve the performance, we choose the dynamic
Figure 9.6
Performance improvement of a closed-loop system using a dynamic controller. (a) Unity feedback closed-loop system; (b) Nyquist diagrams of Gð j!Þ and Gc ð j!ÞGð j!Þ; (c) time response of an open- and closed-loop system; (d) frequency response of an open- and closed-loop system.
372
Chapter 9
Figure 9.7
Nyquist diagrams of Gð j!Þ and Gc ð j!ÞGð j!Þ.
controller Gc ð j!Þ such that Gc ð j!ÞGð j!Þ is tangent to the same circle M but at the frequency !2 , where !2 > !1 . In Figures 9.6c and d, the waveforms M1 ð!Þ and y1 ðtÞ correspond to the open-loop system and the waveforms M2 ð!Þ and y2 ðtÞ correspond to the closed-loop system. From these waveforms we conclude that the closed-loop system has a wider bandwidth than the open-loop system and as a result it is a faster system than the open-loop system. The maximum value M ¼ Mp for both systems is the same. Thus, as a result of the introduction of the controller Gc ð j!Þ, the speed of response of the closed-loop system is greatly improved since it is much faster than that of the open-loop system. Another typical design example is the case of making an unstable system stable. To this end, consider the unstable system with transfer function GðsÞ ¼
1 s2 ðsT þ 1Þ
Its Nyquist diagram is given in Figure 9.7. Here, the control design problem is to find an appropriate Gc ðsÞ so that the closed-loop system becomes stable. This can be done if Gc ðsÞ is chosen such that the Nyquist diagram of Gc ð j!ÞGð j!Þ takes the particular form shown in Figure 9.7. Clearly, such a choice of Gc ðsÞ makes the closed-loop stable.
9.3
CLOSED-LOOP SYSTEM SPECIFICATIONS
The desired improvement of a system’s behavior can be specified either in the time domain, the frequency domain, or in both domains. In the time domain the requirements are specified on the basis of the output function yðtÞ, and they refer mainly to the transient and the steady-state response of yðtÞ. In the case of the transient response it is desired that the system responds as fast as possible (and rarely more slowly), i.e., we want a short rise time, while the overshoot should be kept small. In the case of the steady-state response, it is desired that the error in the steady state (see Sec. 4.7) be zero, and if this is not possible, made as small as possible.
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373
In the frequency domain the specifications are given on the basis of the Nyquist, Bode, or Nichols diagrams of the transfer function Gc ðsÞGðsÞ and they mainly refer to the gain and phase margins and to the bandwidth. In the case of gain and phase margins, it is desirable to have large margins to guarantee sufficient relative stability. In the case of the bandwidth, we seek to make it as wide as possible to reduce the rise time. Certain of the aforementioned specifications are equivalent or conflicting (opposing). Equivalent requirements are, for example, the short rise time and wide bandwidth, since wide bandwidth results in short rise time and vice versa (see Secs 4.3 and 8.3). Conflicting specifications are the cases where as one tries to improve one requirement one does damage to the other and vice versa. Such specifications are, for example, the small steady-state error and the large gain and phase margins. Here, in order to obtain a small steady-state error, the open-loop transfer function Gc ðsÞGðsÞ must have a big amplification factor or many integrations or both, as opposed to obtaining large gain and phase margins, which require small amplification and no integrations (see Sec. 4.7 and Subsec. 8.4.5). Other examples of conflicting specifications are the steady-state and the transient response of yðtÞ, because as the steady-state error improves, i.e., as the steady-state error decreases, the closed-loop system tends to become unstable with its transient response becoming oscillatory. In our effort to improve the behavior of a system we are often confronted with conflicting desired specifications, a common problem in all branches of engineering. In this situation the classical control theory deals with the problem by appropriately comprising the conflicting specifications. Modern control theory uses, for the same purpose, the minimization of a cost function which refers to one or more requirements—for example, the minimization of time and/or energy (see chap. 11). Classical control theory compromises the conflicting specifications most often by using controllers, which are composed of an amplifier with an amplifications constant K in series with electric circuits (as well as other types of circuits such as hydraulic and pneumatic) connected in such a way that the transfer function Gc ðsÞ of the controller has the general form d Y
Gc ðsÞ ¼ K
ð1 þ Ti0 sÞ
i¼1 r Y
ð9:3-1Þ
ð1 þ Ti sÞ
i¼1
The circuits used to realize Gc ðsÞ are called controller circuits. The most common controller circuits used include the phase-lead, the phase-lag, the phase lag-lead, the bridged T, the proportional (P), the proportional plus derivative (PD), the proportional plus integral (PI), and the proportional plus integral plus derivative (PID). Since later in this chapter we will use these circuits for the realization of controllers, we next give a short description of these circuits. Note that these controller circuits are also known in the literature as compensating networks, since they are inserted in the closed-loop system to compensate for certain undesirable performances appearing in the uncompensating closedloop system.
374
9.4
Chapter 9
CONTROLLER CIRCUITS
9.4.1
Phase-Lead Circuit
The most common phase-lead circuit is the simple circuit shown in Figure 9.8. When this circuit is excited by a sinusoidal signal, the phase of the output signal leads the phase of the input signal, i.e., the circuit introduces a ‘‘positive’’ phase. For this reason it is called a phase-lead circuit. Using Figure 9.8 one may readily determine the transfer function Gc ðsÞ of the phase-lead circuit as follows Gc ðsÞ ¼
YðsÞ R2 þ R1 R2 Cs 1 þ aTs ¼ ¼ a1 ¼ a1 G c ðsÞ UðsÞ R1 þ R2 þ R1 R2 Cs 1 þ Ts
ð9:4-1Þ
where G c ðsÞ ¼
1 þ aTs ; 1 þ Ts
a¼
R1 þ R2 > 1; R2
and
T¼
R1 R2 C R1 þ R2
Hence Gc ðsÞ has a real zero at s ¼ 1=aT and a real pole at s ¼ 1=T. Since a > 1, the pole is always to the left of the zero. The Gc ðsÞ diagram for s ¼ j!, i.e., the Nyquist diagram of Gc ðsÞ, is a semicircle. Indeed, if we set s ¼ j! and u ¼ T!, G c ðsÞ becomes G c ð j!Þ ¼ aGc ð j!Þ ¼
1 þ jaT! 1 þ jau ¼ ¼ Re G c ð j!Þ þ j Im G c ð j!Þ ¼ x þ jy 1 þ jT! 1 þ ju ð9:4-2Þ
Equation (9.4-2) is further written as " # ð1 þ jauÞð1 juÞ 1 þ au2 ða 1Þu x þ jy ¼ ¼ þ j 1 þ u2 1 þ u2 1 þ u2 Equating the real and the imaginary parts of both sides in the above equation yields
Figure 9.8
Typical phase-lead circuit.
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375
1 þ au2 1 þ u2 ða 1Þu y¼ 1 þ u2
x¼
ð9:4-3aÞ ð9:4-3bÞ
From Eq. (9.4-3a), we have 1x u2 ¼ xa If we square both sides of Eq. (9.4-3b) and use Eq. (9.4-4), we have
ð9:4-4Þ
1x 2 2 ða 1Þ u ð1 xÞðx aÞ y2 ¼ ¼ ða 1Þ2 x a 2 ¼ ða 1Þ2 2 2 ð1 þ u Þ ða 1Þ2 1x 1þ xa ¼ x a x2 þ ax or x2 ða þ 1Þx þ y2 ¼ a or
x
1þa 2 2 a1 2 þy ¼ 2 2
ð9:4-5Þ
Equation (9.4-5) represents a circle with center at the point ðð1 þ aÞ=2; 0Þ and radius ða 1Þ=2. However, due to Eqs (9.4-3a and b), x and y are always positive, and hence Eq. (9.4-5) refers only to positive x and y. Such semicircles are given in Figure 9.9. Note that Figure 9.9 is the same as that of the right semicircle of the diagram of Figure 8.21e, with K ¼ 1 and T 0 ¼ aT > T ¼ T1 , where a > 1. For large values of the parameter a, the denominator of G c ðsÞ will be smaller than the numerator. Therefore, as a ! þ1 then G c ðsÞ ! 1 þ aTs and, hence, G c ðsÞ becomes a straight line, as can be seen in figure 9.9. In this case G c ðsÞ has two terms: an analog and a differential term. For this reason this controller is called a proportional plus derivative (PD) controller. The phase ’ of G c ðsÞ and, consequently, of Gc ðsÞ is given by ða 1Þu ð9:4-6Þ ’ ¼ tan1 au tan1 u ¼ tan1 1 þ au2 The angle ’m will have its maximum value when d’ a 1 ¼ ¼0 2 2 du 1 þ a u 1 þ u2
ð9:4-7Þ
Equation (9.4-7) is satisfied when 1 u ¼ um ¼ pffiffiffi a
or
and the maximum angle is a1 ’m ¼ tan1 pffiffiffi 2 a
! ¼ !m ¼
1 pffiffiffi T a
ð9:4-8Þ
ð9:4-9Þ
376
Chapter 9
Figure 9.9
Nyquist diagram of the phase-lead circuit transfer function G c ð j!Þ.
From Eq. (9.4-9), we have sin ’m ¼
a1 aþ1
ð9:4-10Þ
Equation (9.4-10) is very useful for the calculation of the suitable value of a for the maximum leading phase. The relation between ’m and a is given in Figure 9.10. The Bode diagrams of the transfer function G c ðsÞ are obtained in the usual way and are presented in Figure 9.11. 9.4.2
Phase-Lag Circuit
The most common phase-lag circuit is the simple circuit shown in Figure 9.12. When this circuit is excited by a sinusoidal signal, the phase of the output signal is lagging
Figure 9.10
The maximum angle ’m as a function of parameter a for the phase-lead circuit.
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377
Bode diagrams of the phase-lead circuit transfer function G c ð j!Þ. (a) Magnitude Bode diagram of G c ð j!Þ; (b) phase Bode diagram of G c ð j!Þ.
Figure 9.11
the phase of the input signal, i.e., the circuit introduces a ‘‘negative’’ phase. For this reason it is called a phase-lag circuit. Using Figure 9.12 one may readily determine the transfer function Gc ðsÞ of the phase-lag circuit as follows: Gc ðsÞ ¼
YðsÞ 1 þ R2 Cs 1 þ aTs ¼ ¼ UðsÞ 1 þ ðR2 þ R2 ÞCs 1 þ Ts
where a¼
R2 <1 R1 þ R2
Figure 9.12
and
T ¼ ðR1 þ R2 ÞC
Typical phase-lag circuit.
ð9:4-11Þ
378
Chapter 9
The transfer function Gc ðsÞ has a real zero at s ¼ 1=aT and a real pole at s ¼ 1=T. Since a < 1, the pole is always to the right of the zero. The Nyquist diagram of Gc ðsÞ is similar to the Nyquist diagram of G c ðsÞ, and is given in Figure 9.13. In the present case, the circles are described by Eq. (9.4-5), with the only difference that x is positive (Eq. (9.4-3a)) but y is negative ((Eq. (9.4-3b)). Such semicircles are given in Figure 9.13. Note that Figure 9.13 is the same as that of the left semicircle of the diagram of Figure 8.21e, with K ¼ 1 and T 0 ¼ aT < T ¼ T1 , where a < 1. The Bode diagrams of the transfer function Gc ðsÞ are obtained in the usual way and are presented in Figure 9.14. 9.4.3
Phase Lag-Lead Circuit
The most common phase lag-lead circuit is given in Figure 9.15. When this circuit is excited by a sinusoidal signal, the phase of the output signal shows a phase lag in the low frequencies and a phase lead in the high frequencies. This circuit combines the characteristics of the phase-lag and the phase-lead circuits studied previously. The transfer function Gc ðsÞ of the phase lag-lead network is given by Gc ðsÞ ¼
ð1 þ R1 C1 sÞð1 þ R2 C2 sÞ 1 þ ðR1 C1 þ R1 C2 þ R2 C2 Þs þ R1 R2 C1 C2 s2
In the special case where ab ¼ 1, Gc ðsÞ can be expressed as the product of G1 ðsÞ and G2 ðsÞ, as follows: 1 þ bT2 s 1 þ aT1 s Gc ðsÞ ¼ ð9:4-12Þ ¼ G1 ðsÞG2 ðsÞ 1 þ T2 s 1 þ T1 s where G1 ðsÞ and G2 ðsÞ are the phase-lag and phase-lead circuit transfer functions, respectively, and where aT1 ¼ R1 C1 , bT2 ¼ R2 C2 , T1 þ T2 ¼ R1 C1 þ R1 C2 þ R2 C2 , and T1 T2 ¼ R1 R2 C1 C2 . To verify this result, multiply the first two equations aT1 ¼
Figure 9.13
Nyquist diagram of the phase-lag circuit transfer function G c ð j!Þ.
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379
Figure 9.14 Bode diagrams of the phase-lag circuit transfer function Gc ðsÞ. (a) Magnitude Bode diagram of Gc ð j!Þ; (b) phase Bode diagram of Gc ð j!Þ. R1 C1 and bT2 ¼ R2 C2 to yield abT1 T2 ¼ R1 R2 C1 C2 . Upon using the last of the four above equations, T1 T2 ¼ R1 R2 C1 C2 , we readily have that ab ¼ 1. Clearly, in order to have the convenient property of Eq. (9.4-12), a and b cannot take any independent aribtrary values, but they are constrained by the equation ab ¼ 1. The Nyquist and Bode diagrams of Gc ðsÞ are shown in Figures 9.16 and 9.17, respectively. From Eq. (9.4-12) and from Figures 9.16 and 9.17, it follows that Gc ðsÞ behaves in the low frequencies as a phase-lag circuit and in the high frequencies as a phase-lead circuit, thus combining the advantages of both phase-lag and phase-lead circuits.
Figure 9.15
Typical phase lag-lead circuit.
380
Chapter 9
Figure 9.16
Nyquist diagram of the phase lag-lead circuit transfer function Gc ð j!Þ.
Remark 9.4.1 A close examination of the three types of controller circuits presented thus far shows that these circuits have, in general, the following design capabilities: 1.
Phase-lead circuits, in the frequency domain, can improve the open-loop transfer function in the high frequencies. In the time domain, they can improve the transient response by decreasing the rise time and the overshoot and to some extent can reduce the steady-state error.
Figure 9.17
Bode diagrams of the phase lead-lag circuit transfer function Gc ðsÞ.
Classical Control Design Methods
2.
3.
9.4.4
381
Phase-lag circuits, in the frequency domain, can improve the open-loop transfer function in the low frequencies. In the time domain, they can improve the steady-state error at the expense of the transient response, because the rise time increases. Phase lag-lead circuits combine the characteristics of phase-lag and phaselead circuits. However, the phase lag-lead circuits increase the order of the system by two compared with phase-lead or phase-lag circuits, which increases the order of the system only by one. This means that although phase lag-lead circuits give better design flexibility than phase-lag and phase-lead circuits, they are not often used because the increase in the order of the system by two complicates the system analysis and design. Bridged T Circuit
The most common bridged T circuit may have one of the two versions given in Figure 9.18. The circuit transfer function of Figure 9.18a is given by G1 ðsÞ ¼
1 þ 2RC2 s þ R2 C1 C2 s2 1 þ RðC1 þ 2C2 Þs þ R2 C1 C2 s2
ð9:4-13Þ
and that of Figure 9.18b is given by G2 ðsÞ ¼
1 þ 2R1 Cs þ C 2 R1 R2 s2 1 þ Cð2R1 þ R2 Þs þ C 2 R1 R2 s2
ð9:4-14Þ
Note that G1 ðsÞ and G2 ðsÞ have essentially the same form. These circuits are different from the phase-lead, phase-lag, and phase lag-lead circuits, because their transfer functions can have complex conjugate zeros which may prove useful in realizing a controller. 9.4.5
Other Circuits
Besides the above four types of circuits, many other types of circuits can be used for controller realization. In Figure 9.19 we give a variety of such circuits.
Figure 9.18
Typical T-bridged circuits.
382
Chapter 9
Figure 9.19
9.5
Controller circuits (continued).
DESIGN WITH PROPORTIONAL CONTROLLERS
In this case the controller is an amplifier of gain K and, hence, Gc ðsÞ ¼ K (Figure 9.20a). Since Gc ðsÞ is constant, one should expect that this type of controller has limited capabilities for improving the system’s performance. Indeed, when Gc ðsÞ ¼ K the only thing that one can do is to increase or decrease K. When increasing K, the steady state of the closed-loop system of Figure 9.20a decreases. This is demonstrated in Figure 9.20b for various types of systems, as well as for various types of
Classical Control Design Methods
Figure 9.19
383
(contd.)
excitation signals. Furthermore, as K increases, the gain margin decreases, and for larger values of K the closed-loop system becomes unstable, as shown in Figure 9.20c. In practice, it is desirable to have a small steady-state error and a large gain margin. These specifications are apparently conflicting, since in improving one we damage the other. In this case, a compromise is usually made, where the steady-state error is small enough, while the gain margin is large enough. A satisfactory such choice for K is K ¼ K1 shown in Figure 9.20c.
384
Chapter 9
Figure 9.20
Proportional controller and its influence upon the behavior of a closed-loop system. (a) Proportional controller in a closed-loop system; (b) steady-state error as a function of K; (c) influence of the parameter K upon the stability of the closed-loop system.
9.6 9.6.1
DESIGN WITH PID CONTROLLERS Introduction to PID Controllers
A PID controller involves three terms: the proportional term designated as Kp , the integral term designated as Ki =s, and the derivative term designated as sKd . Thus, the transfer function of a PID controller has the general form Kp K Kd s2 þ sþ i Kp s þ Ki þ Kd s K Kd Kd ¼ Gc ðsÞ ¼ Kp þ i þ Kd s ¼ s s s 2
ð9:6-1aÞ
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385
where Kp , Ki , and Kd are the proportional, integral, and derivative gains, respectively. PID controllers are also expressed as follows: Kp 1 K þ Td s ; Gc ðsÞ ¼ Kp 1 þ where Ti ¼ and Td ¼ d Ti s Ki Kp ð9:6-1bÞ where Kp is the proportional gain, Ti is called the integration time constant, and Td is called the derivative or rate time constant. The block diagram of the PID controller (Eq. (9.6-1b)) is given in Figure 9.21. Clearly, the transfer function of a PID controller involves one pole at the origin and two zeros whose position depends upon the parameters Kp , Ki , and Kd or Kp , Ti , and Td . The overall problem of PID controllers is how to select (or tune) the arbitrary parameters involved in Gc ðsÞ such as to satisfy the design requirements as best as possible [1]. Special cases of PID controllers are the PI and PD controllers. To facilitate the study of PID controllers, we will first examine the PD and PI controllers. 9.6.2
PD Controllers
The transfer function of a PD controller is given by Kp ¼ Kp ð1 þ Td sÞ Gc ðsÞ ¼ Kp þ Kd s ¼ Kd s þ Kd
ð9:6-2Þ
Consider the closed-loop system of Figure 9.22, where the controller is of the PD type and, for simplicity, the system’s transfer function Gp ðsÞ is of second order. Then, the forward-path transfer function GðsÞ of Figure 9.22 is given by GðsÞ ¼
ðKp þ Kd sÞ YðsÞ ¼ Gc ðsÞGp ðsÞ ¼ EðsÞ sðs þ Þ
ð9:6-3Þ
Thus, in this case, we add to the original system a zero at s ¼ Kp =Kd ¼ 1=Td , but the order of the closed-loop system remains the same. As a result, the closed-loop
Figure 9.21
Block diagram of the PID controller.
386
Chapter 9
Figure 9.22
Closed-loop system with PD controller.
system becomes more stable (see Subsec. 7.5.2). The influence of Gc ðsÞ upon the behavior of the closed-loop system may be interpreted using Figure 9.23. All three waveforms concern a closed-loop system, where the input signal rðtÞ is the step function and the controller is just a proportional controller. We observe that we have a rather high overshoot and the system is quite oscillatory. The waveform of the derivative e_ðtÞ of the error signal eðtÞ gives information about the expected overshoot increase or decrease of yðtÞ. Indeed, in linear systems, if the slope of eðtÞ or of yðtÞ is large, then the overshoot will also be large. Now, if a PD controller were used, then its term sKd predicts this fact and tries to reduce the overshoot. That is, the derivative term of the PD controller acts as an ‘‘anticipatory’’ controller, wherein, by knowing the slope of eðtÞ, the derivative term can anticipate the direction of the error and use it to improve the performance of the closed-loop system. Return to Eq. (9.6-3). For simplicity, set ¼ ¼ 1, in which case the closedloop system transfer function becomes HðsÞ ¼
Kp þ Kd s GðsÞ ¼ 1 þ GðsÞ s2 þ ð1 þ Kd Þs þ Kp
ð9:6-4Þ
To further simplify our study, let Kp ¼ 4 and Kd ¼ 0 and 1. Then, the closed-loop transfer functions H1 ðsÞ and H2 ðsÞ for Kd ¼ 0 and Kd ¼ 1, respectively, become 4 s2 þ s þ 4 4þs H2 ðsÞ ¼ 2 s þ 2s þ 4
H1 ðsÞ ¼
for for
Kd ¼ 0
and
Kd ¼ 1
The response of the closed-loop system for rðtÞ ¼ 1 is given in Figure 9.24. The figure shows that the derivative term reduces the overshoot and damps the oscillations. Example 9.6.1 Consider the orientation control system of a satellite which is described in Subsec. 3.13.7. Examine the behavior of the satellite when the controller Gc ðsÞ is P and PD,
Classical Control Design Methods
Figure 9.23
387
The waveforms of (a) yðtÞ, (b) eðtÞ, and (c) e_ðtÞ.
Figure 9.24 Closed-loop system response of system of Figure 9.22 for ¼ ¼ 1, Kp ¼ 4, and Kd ¼ 0 and 1.
388
Chapter 9
i.e., when Gc ðsÞ ¼ Kp and Gc ðsÞ ¼ Kp ð1 þ Td sÞ. Assume that the input of the closedloop system is r ðtÞ ¼ 1. Solution The angular position of the satellite, when Gc ðsÞ ¼ Kp , is given by (see Eq. (3.13-35)) y ðsÞ ¼ HðsÞr ðsÞ ¼
!2n 1 s ; ¼ 2 2 2 sðs þ !n Þ s s þ !2n
!2n ¼ Kt Kb Kp J 1
Taking the inverse Laplace transform, we have y ðtÞ ¼ 1 cos !n t. This expression for y ðtÞ shows that the output of the system is an undamped oscillation of the satellite about the x-axis. This is because there is no friction in space (i.e., the damping ratio is ¼ 0). Therefore, the system with Gc ðsÞ ¼ Kp , is not behaving satisfactory at all! To improve its behavior, we introduce the PD controller Gc ðsÞ ¼ Kp ð1 þ Td sÞ For this case, the transfer function of the closed-loop system is given by HðsÞ ¼
Kt Kb Kp ð1 þ Td sÞ !2n ð1 þ Td sÞ ; ¼ Js2 þ Kt Kb Kp ð1 þ Td sÞ s2 þ 2!n s þ !2n
!2n ¼ Kt Kb Kp J 1
where ¼ Td !n =2. Let r ðsÞ ¼ 1=s. Then the ouput of the system is given by y ðsÞ ¼ HðsÞr ðsÞ ¼
HðsÞ !2 ð1 þ Td sÞ ¼ 2 n s sðs þ 2!n s þ !2n Þ
If we select a value for the constant Td such that 0 < < 1, then according to case 2 of Subsec. 4.3.2 and Eq. (2.4-7), we have y ðsÞ ¼
1 s þ 2 þ !2n Td ; s ðs þ Þ2 þ !2d
¼ !n ;
and
!d ¼ !n ð1 2 Þ1=2
or " #" # 1 sþ þ !2n Td !d y ðsÞ ¼ s ðs þ Þ2 þ !2d !d ðs þ Þ2 þ !2d The response in the time domain will be " # þ !2n Td t cos !d t þ sin !d t ¼ 1 C et sinð!d t þ ’Þ y ðtÞ ¼ 1 e !d where C¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !2d þ ð þ !2n Td Þ2 !d
and
’ ¼ tan1
!d þ !2n Td
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389
From the above expression for y ðtÞ we see that, when a PD controller is used, the response of the system will be as in Figure 9.25. In this case, the system will exhibit some damped oscillations and will settle in the position y ¼ r ¼ 1. The amplitude of these oscillations is influenced by the damping ratio (Figure 4.4) and, therefore, it can be adjusted from the constants Td and !n , since ¼ Td !n =2, where 0 < < 1 and !2n ¼ Kt Kb Kp J 1 . The present example reveals in a very clear way the influence of the PD controller on the behavior of a closed-loop system. Here, when Gc ðsÞ ¼ Kp the system contantly oscillates, whereas when Gc ðsÞ ¼ Kp ð1 þ Td sÞ the system quickly comes to a standstill. This is solely due to the differential term, which by anticipating the direction of the error, acts accordingly, and reduces the overshoot and the oscillations and brings the system to the desired standstill position y ¼ r ¼ 1. 9.6.3
PI Controllers
The transfer function of a PI controller is given by Ki Kp s þ Kp K 1 Gc ðsÞ ¼ Kp þ i ¼ ¼ Kp 1 þ Ti s s s
ð9:6-5Þ
Consider the closed-loop system of Figure 9.26, where the controller is of the PI type and, for simplicity, the system’s transfer function Gp ðsÞ is of second order. Then, the forward-path transfer function GðsÞ of Figure 9.26 is GðsÞ ¼
ðKi þ Kp sÞ YðsÞ ¼ Gc ðsÞGp ðsÞ ¼ 2 EðsÞ s ðs þ Þ
ð9:6-6Þ
Thus, the PI controller adds to the original system a zero at s ¼ Ki =Kp ¼ 1=Ti and a pole at s ¼ 0. Here, as compared with the PD controller, the order of the system increases by one. We also have an increase by one in the system type (see subsection 4.7.1). As a result, the PI controller has a beneficiary influence on the
Figure 9.25 Time response of the closed-loop system for the control of a satellite orientation system when Gc ðsÞ ¼ Kp ð1 þ Td sÞ.
390
Chapter 9
Figure 9.26
Closed-loop system with PI controller.
steady-state error, since it increases the type of the system by one (see Sec. 4.7). On the contrary, the relative stability decreases due to the pole s ¼ 0 (see Subsec.7.5.1). In particular, the closed-loop response of the system for ¼ ¼ 1, and ðKp Þ1 > ðKp Þ2 > ðKp Þ3 and for ðKi Þ1 > ðKi Þ2 > ðKi Þ3 is shown in Figure 9.27. This figure shows that as the gains Kp and Ki decrease, the oscillations decrease and the system becomes more stable. Appropriate choice of Kp and Ki may yield the desired behavior, which is usually an overshoot of about 25%. Overall, the PI controllers behave as low-frequency filters that improve the steady-state error, but they decrease the relative stability. Example 9.6.2 Consider a system under control, with transfer function GðsÞ ¼
Figure 9.27
K 1 þ T1 s
Closed-loop system response of system of Figure 9.26.
Classical Control Design Methods
391
Show that by using the PI controller (Eq. (9.6-5)), one can achieve arbitrary pole placement and simultaneously drive the position steady-state error to zero. Solution Clearly, by choosing a PI controller, the type of the system becomes one, which guarantees zero position steady-state error (see Sec. 4.7). With regard to pole placement, the transfer function HðsÞ of the closed-loop system is given by HðsÞ ¼
Gc ðsÞGðsÞ 1 þ Gc ðsÞGðsÞ
We have
1 þ Gc ðsÞGðsÞ ¼ 1 þ
K 1 þ T1 s
1 bðsÞ Kp 1 þ ¼ Ti s aðsÞ
The characteristic equation bðsÞ ¼ 0 is as follows: KKp KKp 1 s2 þ sþ þ ¼0 T1 T1 Ti T1 Hence, the closed-loop system is a second-order system. The general form of the characteristic equation of a second-order system is s2 þ 2!n s þ !2n ¼ 0 Equating coefficients of like powers of s in the last two equations and solving for the two PI controller parameters – namely, for the parameters Kp and Ti – we get Kp ¼
2!n T1 1 K
and
Ti ¼
2!n T1 1 !2n T1
Hence, for any desired values of and !n we can always find Kp and Ti such that the closed-loop system has the desired characteristic polynomial s2 þ 2!n s þ !2n . Since the roots of this polynomial are the poles of the closed-loop system, it follows that we can achieve arbitrary pole placement of the given first-order system with a PI controller. This result is of paramount importance, since controlling the poles one controls completely the stability and may greatly influence the time response of the closed-loop system. From the above expressions for Kp and Ti , we observe the following: 1. 2.
9.6.4
The gain Kp is positive for !n > 2T1 . For large values of !n , Ti ffi 2=!n . In this case, Ti does not depend on the system’s time constant T1 . PID Controllers
A typical closed-loop system involving a PID controller is given in Figure 9.28. The PID controller transfer function Gc ðsÞ defined in Eq. (9.6-1b) may further be written as 1 ðas þ 1Þðbs þ 1Þ ð9:6-7aÞ þ Td s ¼ Kp Gc ðsÞ ¼ Kp 1 þ Ti s s
392
Chapter 9
Figure 9.28
Closed-loop system with PID controller.
where a þ b ¼ Ti
and
ab ¼ Ti Td
ð9:6-7bÞ
Hence, the PID controller increases the number of zeros by two and the number of poles by one, where the two zeros are located at s ¼ 1=a and s ¼ 1=b and the pole is located at s ¼ 0. The PID controller is designed by properly choosing Kp , Ki , and Kd , or Kp , Ti , and Td such as to control the system with all the advantages of the PD and PI controllers combined. The resulting desired closed-loop system’s behavior is shown in Figure 9.29. Here, yðtÞ has a small rise time, a small overshoot, a small settling time, and a zero steady-state error. Such a response is, of course, close to the ideal. The difficulty in achieving such a response is the selection (or tuning) of the appropriate Kp , Ki , and Kd for any specific system under control [1]. Two practical methods for selecting the appropriate Kp , Ki , and Kd are presented in Subsec. 9.6.5 that follows.
Figure 9.29
Response of a closed-loop system with PID controller when rðtÞ ¼ 1.
Classical Control Design Methods
393
Example 9.6.3 Consider a system under control, with transfer function GðsÞ ¼
K ð1 þ sT1 Þð1 þ sT2 Þ
Show that using the PID controller (Eq. (9.6-1)), one can achieve arbitrary pole placement and simultaneously drive the position steady-state error to zero. Solution Clearly, by choosing a PID controller, the type of the system becomes one, which guarantees zero position steady-state error (see Sec. 4.7). With regard to pole placement, we work as in Example 9.6.2, and find that the characteristic equation of the closed-loop system is as follows: KKp Td 2 KKp KKp 1 1 1 3 þ þ þ ¼0 s þ s þ sþ T1 T2 T1 T2 T1 T2 T1 T2 T1 T2 Ti Hence, the closed-loop system is a third-order system. The general form of the characteristic equation of a third-order system is ðs þ !Þðs2 þ 2!n s þ !2n Þ ¼ 0 Equating coefficients of like powers of s in the last two characteristic equations, and after some algebraic manipulations, we arrive at the following values for the PID controller parameters Kp , Ti , and Td : Kp ¼
T1 T2 !2n ð1 þ 2Þ 1 ; K
Td ¼
T1 T2 !n ð þ 2Þ T1 T2 T1 T2 !ð1 þ 2Þ 1
Ti ¼
T1 T2 !2n ð1 þ 2Þ 1 ; T1 T2 !3n
Hence, for any desired values of , !n , and we can always find Kp , Ti , and Td such that the closed-loop characteristic polynomial has the desired form ðs þ !n Þðs2 þ 2!n s þ !2n Þ. Since the roots of this polynomial are the poles of the closed-loop system, it follows that we can achieve arbitrary pole placement of the given second-order system with a PID controller. This result is of paramount importance, since controlling the poles one controls completely the stability and may greatly influence the time response of the closed-loop system. Example 9.6.4 Consider the closed-loop system shown in Figure 9.30, where a PID controller is used to control the direction of a ship. The disturbance (e.g., due to the wind) affects the system, as shown in the block diagram. The input reference signal rðtÞ is usually constant. Design a control system such that the closed-loop response to a unit step disturbance decays fast (e.g., the settling time Ts is 2 or 3 sec with 2% final value tolerance) and the damping of the system is satisfactory. Choose the position of the poles so that the closed-loop system has a pair of dominant poles. Find the time response of the system to a unit step disturbance and to a unit step input reference signal.
394
Chapter 9
Figure 9.30
Control system which uses a PID controller to control the direction of a ship.
Solution According to relation (9.6-7), the transfer function of the PID controller can be written as Kðas þ 1Þðbs þ 1Þ 1 ¼ Kp 1 þ þ Td s Gc ðsÞ ¼ s Ti s where K ¼ Kp , a þ b ¼ Ti , and ab ¼ Ti Td . When RðsÞ ¼ 0 and DðsÞ 6¼ 0, the closedloop transfer function due to the disturbance DðsÞ is given by Yd ðsÞ Gc ðsÞGs ðsÞ s ¼ ¼ 2 DðsÞ 1 þ Gc ðsÞGs ðsÞ sðs þ 4s þ 16Þ þ Kðas þ 1Þðbs þ 1Þ s ¼ 3 s þ ð4 þ KabÞs2 þ ð16 þ Ka þ KbÞs þ K where Yd ðsÞ is the output due to the disturbance DðsÞ. The closed-loop specifications require that the response to a unit step disturbance be such that the settling time Ts is 2–3 sec with 2% final value tolerance, and the system has satisfactory damping. However, we have that Ts ¼
4 ¼ 2 sec !0
and therefore we have !0 ¼ 2
We can choose ¼ 0:4 and !n ¼ 5 rad/sec for the dominant poles of the closed-loop system. We choose the third pole at s ¼ 12, so that it has negligible effect on the response. Then, the desired characteristic equation can be written as ðs þ 12Þ½s2 þ 2ð0:4Þ5s þ 52 ¼ ðs þ 12Þðs2 þ 4s þ 25Þ ¼ s3 þ 16s2 þ 73s þ 300 ¼ 0 The characteristic equation of the closed-loop transfer function (having as input the disturbance DðsÞ), is given by s3 þ ð4 þ KabÞs2 þ ð16 þ Ka þ KbÞs þ K ¼ 0 Comparing the coefficients of the two characteristic polynomials, it follows that 4 þ Kab ¼ 16, 16 þ Ka þ Kb ¼ 73, and K ¼ 300. Hence, ab ¼ 0:04 and a þ b ¼ 0:19. Therefore, the PID controller becomes
Classical Control Design Methods
Gc ðsÞ ¼
395
K½abs2 þ ða þ bÞs þ 1 12ðs2 þ 4:75s þ 25Þ ¼ s s
Using this PID controller, the response Yd ðsÞ to the disturbance DðsÞ is s Yd ðsÞ ¼ DðsÞ ðs þ 12Þðs2 þ 4s þ 25Þ For DðsÞ ¼ 1=s, the output in the steady state is zero, since, according to the final value theorem, we have " # s2 1 ¼0 lim yd ðtÞ ¼ lim sYd ðsÞ ¼ lim 2 t!1 s!0 s!0 ðs þ 12Þðs þ 4s þ 25Þ s More specifically, we have " # s2 1 0:099174 0:099174s þ 0:206612 þ Yd ðsÞ ¼ ¼ s þ 12 s2 þ 4s þ 25 ðs þ 12Þðs2 þ 4s þ 25Þ s pffiffiffiffiffi 0:099174 0:099174ðs þ 2Þ 0:08837 21 pffiffiffiffiffi þ pffiffiffiffiffi ¼ s þ 12 ðs þ 2Þ2 þ ð 21 Þ2 ðs þ 2Þ2 þ ð 21 Þ2 Taking the inverse Laplace transform, we have hpffiffiffiffiffi i hpffiffiffiffiffi i yd ðtÞ ¼ 0:099174e12t 0:099174e2t cos 21 t þ 0:08837e2t sin 21 t The unit step response is shown in Figure 9.31, from which we get a settling time around 0.72 sec and a satisfactory damping. Therefore, the design with regard to the disturbance is satisfactory. When RðsÞ 6¼ 0 and DðsÞ ¼ 0, the closed-loop transfer function due to the input reference signal RðsÞ is given by
Figure 9.31
Closed-loop response to unit step disturbance.
396
Chapter 9
Yr ðsÞ Gc ðsÞGs ðsÞ 12ðs2 þ 4:75s þ 25Þ ¼ ¼ 3 RðsÞ 1 þ Gc ðsÞGs ðsÞ s þ 16s2 þ 73s þ 300 For RðsÞ ¼ 1=s, the output Yr ðsÞ becomes " # 12ðs2 þ 4:75s þ 25Þ 1 Yr ðsÞ ¼ 3 s þ 16s2 þ 73s þ 300 s ¼
pffiffiffiffiffi 1 0:92562 0:07438ðs þ 2Þ 0:16231 21 p ffiffiffiffiffi pffiffiffiffiffi þ s s þ 12 ðs þ 2Þ2 þ ð 21 Þ2 ðs þ 2Þ2 þ ð 21 Þ2
Taking the inverse Laplace transform, we have hpffiffiffi i hpffiffiffiffiffi i yr ðtÞ ¼ 1 0:92562 e12t 0:07438e2t cos 21 t þ 0:1623 e2t sin 21 t The unit step response is shown in Figure 9.32, from which we find that the maximum overshoot is 7.15% and the settling time is 1.1 sec, which are very satisfactory. 9.6.5
Design of PID Controllers Using the Ziegler–Nichols Methods
The PID controller has the flexibility of simultaneously tuning three parameters: namely, the parameters Kp , Ti , and Td . This allows a PID controller to satisfy the design requirements in many practical cases, a fact which makes the PID controller the most frequently met controller in practice. The appropriate values of the parameters Kp , Ti , and Td of the PID controller may be chosen by trial and error. This is usually a formidable task, even in cases where the design engineer has great experience on the subject. To facilitate the determination of the appropriate values of the parameters Kp , Ti , and Td , even for cases where a mathematical model for the system under control is not available, Ziegler and Nichols [11] have suggested the following two rather simple and practically useful methods. 1 The Transient Response Method In this case, the system under control is excited with the unit step function (Figure 9.33a). The shape of the transient response of the open-loop system may have the
Figure 9.32
Closed-loop response to unit step reference signal.
Classical Control Design Methods
Figure 9.33
397
The transient response method. (a) Experimental step response; (b) detailed
step response.
general form shown in Figure 9.33b. In this case, we introduce the parameters td ¼ delay time and tr ¼ rise time. Aiming in achieving a damping ratio of about 0.2 (which corresponds to an overshoot of about 25%), the values of the parameters Kp , Ti , and Td of the PID controller are chosen according to Table 9.1. It is useful to mention that here the transfer function of the system under control may be approximated as follows td s e GðsÞ ¼ K ð9:6-8Þ 1 þ tr s Furthermore, upon using Table 9.1, the PID controller transfer function Gc ðsÞ becomes 1 tr 1 þ Td s ¼ 1:2 þ 0:5td s Gc ðsÞ ¼ Kp 1 þ 1þ Ti s 2td s td " # ðs þ 1=td Þ2 ¼ ð0:6tr Þ ð9:6-9Þ s That is, the PID controller has a pole at the origin and a double zero at s ¼ 1=td .
398
Chapter 9
Table 9.1 The Values of the Parameters Kp , Ti , and Td Using the Ziegler–Nichols Transient Response Method Controller
Kp
Ti
Td
1
0
Proportional
P
tr td
Proportional–integral
PI
0:9
tr td
td 0:3
0
Proportional–integral–derivative
PID
1:2
tr td
2td
0:5td
Example 9.6.5 In Figure 9.34, the unit step response of a plant is given. Determine Kp , Ki , and Kd of a PID controller using the transient response method. Solution From the figure we have that td ¼ 150 sec and tr ¼ 75 sec. Using Table 9.1, we readily have Kp ¼ 1:2
tr 75 ¼ 0:6 ¼ ð1:2Þ 150 td
Ti ¼ 2td ¼ 2ð150Þ ¼ 300 sec Td ¼ 0:5td ¼ ð0:5Þð150Þ ¼ 75 sec 2 The Stability Limit Method Here, we start by controlling the system only with a proportional controller (Figure 9.35a). The gain Kp is slowly increased until a persistent oscillation is reached (Figure 9.35b). At this point, we mark down the value of the parameter Kp , denoted as K~ p , as
Figure 9.34
The unit step response of a plant of Example 9.6.5.
Classical Control Design Methods
399
Figure 9.35 The stability limit method. (a) Closed-loop system with proportional controller; (b) sustained oscillations with period T~ . well as the value of the respective oscillation period, denoted as T~ . Then, the parameters Kp , Ti , and Tp of the PID controller are chosen according to Table 9.2. For the present case, and upon using Table 9.2, the transfer function of the PID controller becomes ! 1 1 T~ ~ þ Td s ¼ 0:6Kp 1 þ Gc ðsÞ ¼ Kp 1 þ þ Ti s 0:5T~ 8 " # ðs þ 4=T~ Þ2 ¼ ð0:075K~ p T~ Þ ð9:6-10Þ s That is, the PID controller has a pole at the origin and a double zero at s ¼ 4=T~ . Example 9.6.6 The position control system of an object holder robot is shown in Figure 9.36. The block diagram is given in Figure 9.37. Use the stability limit method of ZieglerNichols to determine the parameters of the PID controller. Use the Routh stability criterion to calculate the values of K~ p and T~ . Table 9.2 The Values of the Parameters Kp , Ti , and Td Using the Ziegler–Nichols Stability Limit Method Controller Proportional Proportional–integral Proportional–integral–derivative
P PI PID
Kp
Ti
Td
0:5K~ p 0:45K~ p 0:6K~ p
1 T~ =1:2 T~ =2
0 0 T~ =8
400
Chapter 9
Figure 9.36
Object holder robot.
Solution Using only a proportional controller, as in Figure 9.35a, yields the following closedloop transfer function Kp YðsÞ ¼ RðsÞ sðs þ 1Þðs þ 4Þ þ Kp The value of Kp that makes the system marginally stable, in which case sustained oscillations occur, can be obtained using Routh’s stability criterion. The characteristic equation of the closed-loop system is given by s3 þ 5s2 þ 4s þ Kp ¼ 0 The Routh array is as follows: s3 1 4 5 Kp s2 20 Kp s1 5 Kp s0
Figure 9.37
Position control system with PID controller of an object holder robot.
Classical Control Design Methods
401
Examining the coefficients of the first column of the Routh table, we find that sustained oscillation will occur when Kp ¼ 20. Thus, the critical gain K~ p is 20. With the gain Kp set equal to K~ p , the characteristic equation becomes s2 þ 5s2 þ 4s þ 20 ¼ 0 Substituting s ¼ j! into the characteristic equation, we can find the frequency of the sustained oscillations. We have ð j!Þ3 þ 5ð j!Þ2 þ 4j! þ 20 ¼ 0 or 5ð4 !2 Þ þ j!ð4 !2 Þ ¼ 0
and hence
!2 ¼ 4
or
!¼2
The period T~ of the sustained oscillations is 2 2 ¼ ¼ ¼ 3:14 T~ ¼ ! 2 Referring to Table 9.2, we determine Kp , Ti , and Td as follows: Kp ¼ 0:6K~ p ¼ ð0:6Þð20Þ ¼ 12 Ti ¼ 0:5T~ ¼ ð0:5Þð3:14Þ ¼ 1:57 Td ¼ 0:125T~ ¼ ð0:125Þð3:14Þ ¼ 0:3925 Hence, the transfer function of the PID controller is the following: 1 1 þ 0:3925s þ Td s ¼ 12 1 þ Gc ðsÞ ¼ Kp 1 þ Ti s 1:57s ¼
4:71ðs þ 1:27389Þ2 s
The PID controller has a pole at the origin and double zero at s ¼ 1:27389. The transfer function HðsÞ of the closed-loop system is given by " # 4:71ðs þ 1:27389Þ2 1 sðs þ 1Þðs þ 4Þ s Gc ðsÞGr ðsÞ " # HðsÞ ¼ ¼ 2 1 þ Gc ðsÞGr ðsÞ 4:71ðs þ 1:27389Þ 1 1þ sðs þ 1Þðs þ 4 s ¼
4:71s2 þ 12s þ 7:643 s þ 5s3 þ 8:71s2 þ 12s þ 7:643 4
The unit step response YðsÞ of the closed-loop system is given by #" # " # " 1 4:71s2 þ 12s þ 7:643 1 YðsÞ ¼ HðsÞRðsÞ ¼ HðsÞ ¼ 4 s s þ 5s3 þ 8:71s2 þ 12s þ 7:643 s The unit step response of the closed-loop system is shown in Figure 9.38. If the maximum overshoot is excessive, it can be reduced by fine tuning the controller parameters.
402
Chapter 9
Figure 9.38
9.6.6
Closed-loop response to unit step reference signal.
Active Circuit Realization for PID Controllers
In Sec. 9.4, several circuits were presented, which are the circuit realizations of many popular controllers, such as phase-lead, phase-lag, phase lag-lead, etc. These realizations involve the passive elements R, L, and C and for this reason they are called passive circuit controller realizations. Here, we will present another approach to controller realization by using an active element, namely, the operational amplifier. A typical such circuit involving an operational amplifier is given in Figure 9.39. Since the operational amplifier is an active element, it is for this reason that such realization is called active-circuit realization of a controller, as compared with passive-circuit realizations presented in Sec. 9.4. The active circuit of Figure 9.39 appears to have great flexibility in realizing many types of controllers, by making the proper choice of Z1 ðsÞ and Z2 ðsÞ. Note that here the input voltage Vi ðsÞ is related to the output voltage V0 ðsÞ via the transfer function Gc ðsÞ ¼
V0 ðsÞ Z ðsÞ ¼ 2 Vi ðsÞ Z1 ðsÞ
ð9:6-11Þ
Figure 9.40 presents several configurations which lead to active realizations for various types of controllers, including PI, PD, and PID.
Figure 9.39
Active-circuit realization of a controller.
Classical Control Design Methods
Figure 9.40 9.7
403
Active-circuit realization of controllers.
DESIGN WITH PHASE-LEAD CONTROLLERS
Phase-lead controllers are used to introduce a positive phase in the closed-loop transfer function, aimed at improving the transition response of the system in the time domain (i.e., decreasing the rise time and overshoot) and in the frequency domain at improving the gain and phase margins and the bandwidth (i.e., increasing Kg , ’p , and BW). The transfer function G c ðsÞ defined in Eq. (9.4-1) of the phase-lead
404
Chapter 9
circuit influences the open-loop transfer function at the high frequencies. This is easily seen if we consider G c ðsÞ to have the special form G c ðsÞ ¼ 1 þ sKd
ð9:7-1Þ
Equation (9.7-1) is the special case of G c ðsÞ defined by Eq. (9.4-1) as a ! þ1 (see Figure 9.9). In this case we observe that the controller G c ðsÞ ¼ 1 þ sKd involves two terms: the proportional term 1 and the differential term sKd . Hence, G c ðsÞ is a PD controller. For this reason we say that the phase-lead controller, as a ! 1, behaves like a PD controller. A simple way of realizing G c ðsÞ ¼ 1 þ sKd is given in Figure 9.41a. The influence of G c ð j!Þ ¼ 1 þ j!Kd upon the transfer function Gð j!Þ of a given system under control is shown in Figure 9.41b, wherein the closed-loop sytem becomes stable. When the G c ð j!Þ does not have the special form of 1 þ j!Kd , but instead has the general form G c ð j!Þ ¼ ½ð1 þ ja!TÞ=ð1 þ j!TÞ, whose Nyquist diagram is a semicircle as shown in Figure 9.9, then G c ð j!Þ is actually an approximation of 1 þ j!Kd and, as a result, it influences the diagram of G c ð jÞGð j!Þ in approximately the same way as in Figure 9.41b.
Figure 9.41
Closed-loop system with a phase-lead controller and the influence of this controller on the Nyquist diagram of the closed-loop system. (a) Closed-loop system with controller G c ðsÞ ¼ l þ sKd ; (b) Nyquist diagram of Gðj!Þ; G c ðj!Þ; and G c ðj!ÞGðj!Þ.
Classical Control Design Methods
405
The design of a phase-lead controller is done preferably with the use of Bode diagrams, since the influence of the controller is calculated by simply adding the amplitude and phase diagrams of the controller to the corresponding diagrams of the given system. Let the design specifications refer only to the steady-state error and the gain and phase margins. Also let the open-loop transfer function involve an amplification factor K. Then, the main steps in determining the amplification constant K and the transfer function Gc ðsÞ ¼ a1 G c ðsÞ of the controller are 1. 2.
3. 4.
The amplification constant K is chosen so as to compensate for the parameter a1 and to satisfy the specifications of the steady-state error. Using the bode diagrams of the transfer function Gð j!Þ of the system under control, the angle ’m of G c ð j!Þ is determined, which must be added to the system in order to satisfy the phase margin specifications. On the basis ’m , the parameter a of G c ð j!Þ is determined by using Eq. (9.410) or from Figure 9.10. The parameter T is calculated in such a way that, for ! ¼ !m , 20 log jGð j!m Þj ¼ 0:5½20 log a. Since !m is the geometric mean of the corner frequencies 1=aT and 1=T, we can conclude that the parameter T is calculated from the equation T¼
5.
1 pffiffiffi !m a
ð9:7-2Þ
Finally, we draw the compensated open-loop diagram Gc ðsÞGðsÞ. If the value of the parameter T does not give satisfactory results we repeat the above steps by giving new (usually bigger) values to the parameter ’m until we get satisfactory results.
The above procedure for determining K and Gc ðsÞ, is illustrated in the example that follows. Example 9.7.1 Consider the sun-seeker control system shown in Figure 1.21 in Chap. 1. The block diagram of this system is given in Figure 9.42. The transfer function of each block is as follows: G1 ðsÞ ¼ Is ;
G2 ðsÞ ¼ Ka ;
G3 ðsÞ ¼
Ki ; sðRa Jm s þ Ra Bm þ Ki Kb Þ
G4 ðsÞ ¼ K4
Figure 9.42
Block diagram of the sun-seeker orientation control system.
and
406
Chapter 9
Note that the transfer function G3 ðsÞ of the motor is given by Eq. (3.12-17) when La ! 0. The open-loop transfer function GðsÞ will then be GðsÞ ¼ G1 ðsÞG2 ðsÞG3 ðsÞG4 ðsÞ ¼
Is K a K i K 4 sðRa Jm s þ Ra Bm þ Ki Kb Þ
Typical values for the parameters of GðsÞ are approximately Ra Jm ¼ 1, Ra Bm þ Ki Kb ¼ 10, and Is Ki K4 ¼ 103 . For simplicity let K ¼ Ka . Then GðsÞ takes on the form GðsÞ ¼
103 K sðs þ 10Þ
The transfer function HðsÞ of the closed-loop system will be HðsÞ ¼
103 K s2 þ 10s þ 103 K
Let the angular position of r ðtÞ of the sun be given by r ðtÞ ¼ t Since the Laplace transform of t is 1=s2 , the output y ðsÞ becomes " # 103 K 1 y ðsÞ ¼ HðsÞr ðsÞ ¼ 2 3 s þ 10s þ 10 K s2 The inverse Laplace transformation of y ðsÞ has the general form y ðtÞ ¼ C0 þ C1 t þ C2 et sinð!t þ ’Þ where C1 ¼ 1. The constant C0 is the velocity steady error which y ðtÞ presents in comparison to r ðtÞ ¼ t (Figure 9.43). The constant C0 is calculated according to Eq. (4.7-11), as follows:
Figure 9.43
The steady-state error of the system of Figure 9.42 when r ðtÞ ¼ t.
Classical Control Design Methods
C0 ¼ ess ðtÞ ¼ lim sEðsÞ ¼ s!0
407
1 1 ¼ lim sGðsÞ 100K
s!0
The design problem here is the following. We are given the closed-loop system of Figure 9.44a. In this system we introduce a phase-lead controller with transfer function Gc ðsÞ (Figure 9.44b). Determine the parameters of Gc ðsÞ, as well as the system amplification constant K, such that the compensated closed-loop system of Figure 9.44b satisfies the following: The velocity steady-state error, when r ðtÞ ¼ t, is equal to or less than 0.01. (b) The phase margin is larger than 408.
(a)
Solution According to Eq. (4.7-11), the velocity error of the compensated closed-loop system of Figure 9.44b is as follows sr ðsÞ 1 1 þ aTs ; Gc ðsÞ ¼ a ess ðtÞ ¼ lim s!0 1 þ Gc ðsÞGðsÞ 1 þ Ts Therefore
9 1 > > s 2 = a s ¼ 102 ess ðtÞ ¼ lim s!0> > 1 þ aTs 1000K 100K 1 > > :1 þ a ; 1 þ Ts sðs þ 10Þ 8 > > <
Thus, we must have K a. For simplicity, let K ¼ a. Then, the open-loop transfer function becomes 1 þ aTs 1000a 1 þ aTs 1000 ¼ ¼ G c ðsÞG ðsÞ Gc ðsÞGðsÞ ¼ a1 1 þ Ts sðs þ 10Þ 1 þ Ts sðs þ 10Þ where
Figure 9.44 The sun-seeker orientation control system (a) without a controller (uncompensated system) and (b) with a controller (compensated system).
408
Chapter 9
G c ðsÞ ¼
1 þ aTs 1 þ Ts
G ðsÞ ¼
and
1000 sðs þ 10Þ
From the above equation we can conclude that when the amplification constant K is selected to be equal to a, then the amplifier compensates the amplitude damping which is introduced by the factor a1 . Hence, the selection of the parameters a and T is facilitated by working with the transfer functions G c ðsÞ and G ðsÞ, rather than the transfer functions Gc ðsÞ and GðsÞ. This is the approach used in the sequel. The critical frequency ! 0 (see Sec. 8.5.4 and Figure 8.40) for which the curve 20 log jG ð j!Þj becomes 0 dB satisfies the relation jG ð j! 0 Þj ¼ 1, i.e., satisfies the relation 102 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ 1 ! 0 ð0:01Þð! 0 Þ2 þ 1
or
ð! 0 Þ2 ½ð0:01Þð! 0 Þ2 þ 1 ¼ 104
From the above equation and Figure 9.45, we find that ! 0 ’ 31 rad/sec. For this frequency we calculate the phase margin ’p for the uncompensated system. We have ’p ¼ 1808 þ
Gð j! 0 Þ ¼ 1808 908 "’1 ð0:1! 0 Þ ¼ 908 "’1 3:1
¼ 908 728 ¼ 188 Since this phase margin is too small, the controller G c ðsÞ must introduce a positive phase in the high frequencies. The phase-lead circuit achieves just that, as we have
Figure 9.45 Phase and amplitude Bode diagrams of the uncompensated system G ð j!Þ and the compensated G ð j!ÞG c ð j!Þ system of Example 9.7.1.
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already explained in Remark 9.4.1 of Sec. 9.4. To satisfy the specification for a phase margin of 408, the phase of the controller transfer function G c ð j!Þ ¼
1 þ j!aT 1 þ j!T
should be 228. Remark 9.7.1 Using the relation 20 log jG ð j!m Þ ¼ 0:5½20 log a, as mentioned in step 4 of the phase-lead controller design approach presented at the beginning of this section, we seek to secure that when ! ¼ !m , the amplitude of G c ð j!ÞG ð j!Þ is zero and its phase is ’m (given by Eq. (9.4-9)). This, unfortunately, is not possible to achieve exactly for the following reason: since !m > ! 0 , the phase of G ð j!Þ at the frequency !m is different (smaller) than the phase at the frequency ! 0 . This means that the correction of 228 that we expect from G c ð j!Þ to have in order to secure a phase margin of 408 at the frequency !m , is no longer valid. To overcome this obstacle, we increase the angle of 228 by an amount analogous to the slope of Gð j!Þ. Using Remark 9.7.1, we increase the phase by 58, in which case the maximum angle ’m of G c ð j!Þ becomes ’m ¼ 278. Then, using Eq. (9.4-10) we can determine the constant a. We have sin ’m ¼ sin 278 ¼
a1 ¼ 0:454 aþ1
thus, a ¼ 2:663. Of course, we would have arrived at the same value for a if we had used Figure 9.10. The frequency !m is calculated from the equation 20 log jG ð j!m Þj ¼ 0:5½20 log a ¼ 4:25. From the plot of 20 log jG ð j!Þj of figure 9.45 we obtain !m ¼ 40 rad/sec. Using the values of a and !m we can specify the time constant T. The corner frequencies 1=aT and 1=T of the Bode curve of G ð j!Þ (Figure 9.11) are selected such that the maximum angle ’m is equal to ’m ¼ G c ð j!m Þ, where !m is the geometrical mean of the two corner frequencies. In other words, we choose 1 !m ¼ pffiffiffi T a Hence T¼
1 pffiffiffi ¼ 0:0153 !m a
Consequently, the transfer function Gc ðsÞ of the phase-lead controller is given by 1 1 þ aTs 1 1 þ 0:04s ¼ Gc ðsÞ ¼ a 1 þ Ts 2:66 1 þ 0:0153s Hence, the open-loop compensated transfer function becomes 1 1 þ 0:045s ð1000Þð2:663Þ 2663ðs þ 24:5Þ ¼ Gc ðsÞGðsÞ ¼ 2:663 1 þ 0:0153s sðs þ 10Þ sðs þ 10Þðs þ 65:2Þ The phase margin for the compensated system (see Figure 9.45) is about 438.
410
Figure 9.46
Chapter 9
Nichols diagrams of the uncompensated and the compensated system of
Example 9.7.1.
In Figure 9.45 we show the phase and magnitude Bode plots of the compensated and uncompensated system. In Figure 9.46, the same plots are shown using Nichols diagrams. In Figure 9.47, we give the time response of those two systems when the input is the unit step function. One can observe the improvement of the transient response of the compensated system (shorter rise time and smaller overshoot). In Figure 9.48 the amplitude M of the two systems is given as a function of
Figure 9.47 9.7.1.
Time response of the uncompensated and the compensated system of Example
Classical Control Design Methods
Figure 9.48
411
The amplitude M of the uncompensated and the compensated system of
Example 9.7.1.
the frequency !, where the effect of the controller on the bandwidth is shown. Finally, in Figure 9.49 the root locus of both systems is given, where the effect of the phase-lead controller upon the root locus of the closed-loop system is clearly shown. 9.8
DESIGN WITH PHASE-LAG CONTROLLERS
The phase-lag controllers are used to introduce a negative phase in the closedloop transfer function aimed at improving the overshoot and the relative stability (note that, the rise time usually increases). The transfer function Gc ðsÞ of the phase-lag circuit affects the closed-loop transfer function in the low frequencies. This is easily seen if one considers the special case where Gc ðsÞ has the form Gc ðsÞ ¼ 1 þ Ki =s. This form of Gc ðsÞ is the phase-lag controller (Eq. (9.4-11)) as a ! 0. This special case is shown in Figure 9.13. Since this special case involves a proportional and an integral term, we say that the phase-lag controller, as a ! 0, behaves like a PI controller. A simple way to realize Gc ðsÞ ¼ 1 þ Ki =s is as in Figure 9.50a. Note that the phase of Gc ð j!Þ is constant and equal to 908. The influence of Gc ðsÞ ¼ 1 þ Ki =s on the closed-loop system performance is shown in Figure 9.50b. The influence of the phase-lag controller Gc ðsÞ ¼ ð1 þ aTsÞ=ð1 þ TsÞ on the closed-loop system performance is quite similar to the influence of Gc ðsÞ ¼ 1 þ Ki =s. The main steps in determining the system amplification constant K of the openloop transfer function and of the parameter of the controller transfer function Gc ðsÞ of the phase-lag controller, using the Bode diagrams, are the following: 1. 2.
The amplification constant K is chosen such as to satisfy the specifications of the steady-state error. From the Bode diagrams of Gð j!Þ we determine the phase and gain margins. Next, we find the frequency ! 0 corresponding to the specified phase margin. By knowing the frequency ! 0 we can determine the value of the parameter a of Gc ð j!Þ such that the magnitude diagram of the open-loop transfer function Gc ð j!ÞGð j!Þ, for ! ¼ ! 0 , becomes 0 dB, i.e., at the frequency ! 0 , we have 20 log jGc ð j! 0 ÞGð j! 0 Þj ¼ 0. We choose Gc ð j!Þ so that the frequency ! 0 is bigger than the corner frequency 1=aT (see Figure
412
Chapter 9
Figure 9.49
The root locus of (a) the uncompensated and (b) the compensated system of
Example 9.7.1.
9.14), in which case 20 log jGc ð j! 0 Þj ¼ 20 log a. Thus, the parameter a may be calculated according to the relation 20 log jGc ð j! 0 Þj ¼ 20 log a ¼ 20 log ajGð j! 0 Þj or according to the relation a ¼ 10 3.
20 log jGð j! 0 Þj 20
The selection of the parameter T is done by approximation: usually, it is selected so that the highest corner frequency 1=aT is 10% of the new critical frequency ! 0 , i.e., we choose 1 !0 ¼ aT 10
4.
ð9:8-1Þ
ð9:8-2Þ
The choice of T is made so that the phase of Gc ðsÞ does not affect the phase of Gð j!Þ at the frequency ! 0 . Finally, we construct the diagram of the compensated open-loop transfer function Gc ðsÞGðsÞ. If the values of the parameters a and T do not give satisfactory results, we repeat step 2 by giving new (usually smaller) values to the critical frequency ! 0 until we get satisfactory results.
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413
Figure 9.50 Closed-loop system with phase-lag controller and the effect of the controller on the Nyquist diagram of the closed-loop system. (a) Closed-loop system with controller Gc ðsÞ ¼ 1 þ ðKi =sÞ; (b) Nyquist diagram of Gð j!Þ, Gc ð j!Þ, and Gc ð j!ÞGðsÞ.
Example 9.8.1 Design a phase-lag controller that it satisfies the design specifications of the system of Example 9.7.1. Solution Consider Figure 9.44b. For the present example, the controller Gc ðsÞ is the phase-lag controller, whose transfer function has the form Gc ðsÞ ¼
1 þ aTs ; 1 þ Ts
a<1
The steady-state velocity error requirement is met by appropriately choosing the amplification constant K using Eq. (4.7-11) as follows: 8 9 1 > > > > s < = si ðsÞ 1 s2 ¼ ess ðtÞ ¼ lim 102 ¼ lim > s!0 1 þ Gc ðsÞGðsÞ s!0> 1 þ aTs 1000K 100K > > ; :1 þ 1 þ Ts sðs þ 10Þ Hence, K 1:
414
Chapter 9
The Bode diagrams of Gð j!Þ are given in Figure 9.51. From these diagrams we can conclude that at the frequency ! ¼ 14 rad/sec the phase of Gð j!Þ is 408. The frequency ! 0 is usually chosen to be smaller than ! ¼ 14 rad/sec for the reasons given in the following remark. Remark 9.8.1 For the case of the phase-lead circuits (as mentioned in Remark 9.7.1) there are certain difficulties in selecting !m for the appropriate maximum angle ’m . Similar difficulties arise for the case of phase-lag circuits in selecting ! 0 which will lead to the appropriate parameter a. This difficulty is handled in the same way as in Remark 9.7.1. Specifically, in place of the frequency ! 0 , we choose a smaller frequency, analogous to the slope of the magnitude diagram of Gð j!Þ, because the critical frequency of Gc ð jÞGð j!Þ will be smaller than the critical frequency ! 0 of Gð j!Þ. This is because in Gc ð j!ÞGð j!Þ the factor Gc ð j!Þ shifts the magnitude plot of Gð j!Þ downwards. Using Remark 9.8.1 we select ! 0 a bit smaller than the value 14 rad/sec. For example, let ! 0 ¼ 10 rad/sec. Then, the magnitude of Gð j!Þ at the frequency ! 0 ¼ 10 rad/sec is about 20 dB. Hence the constant a, according to Eq. (9.8-1), is given by a ¼ 1020 ¼ 0:1 20
The time constant T is calculated according to Eq. (9.8-2). We have 1 ! 0 10 ¼ ¼1 ¼ aT 10 10
Figure 9.51
Bode diagrams of phase and magnitude plots of the uncompensated and the compensated system of Example 9.8.1.
Classical Control Design Methods
Figure 9.52
415
Nichols diagrams of the uncompensated and the compensated system of
Example 9.8.1.
Hence, T ¼ 10. The transfer function Gc ðsÞ of the phase-lag controller has the form Gc ðsÞ ¼
1 þ aTs 1þs ¼ 1 þ Ts 1 þ 10s
The open-loop transfer function of the compensated system becomes Gc ðsÞGðsÞ ¼
100ðs þ 1Þ sðs þ 0:1Þðs þ 10Þ
The phase margin of the compensated system (see Figure 9.51) is close to 508. This margin is about 108 bigger than the required margin of 408, and it is therefore very satisfactory. In Figure 9.52 the Nichols diagrams of Gð j!Þ and Gc ð j!ÞGð j!Þ are given. In Figure 9.53 we present the time response of these two systems when the input is the unit step function, from which it is clear that the compensated system has a smaller overshoot but higher rise time. The increase in the rise time is because the bandwidth of the compensated system has decreased.
9.9
DESIGN WITH PHASE LAG-LEAD CONTROLLERS
The phase lag-lead controllers are used in cases where a phase-lead or a phase-lag controller alone cannot satisfy the design specifications. For the selection of the appropriate phase lag-lead controller there is no systematic method. For this reason, it is usually done by successive approximations. Note that a special form of the transfer function Gc ðsÞ of the phase lag-lead networks is the PID controller, which has been presented in Sec. 9.6.
416
Chapter 9
Figure 9.53
Time response of the uncompensated and the compensated system of Example
9.8.1.
Example 9.9.1 Consider a system with transfer function GðsÞ ¼
K sð1 þ 0:1sÞð1 þ 0:4sÞ
Find a phase lag-lead controller such as to satisfy the following closed-loop specifications: (a) Velocity constant Kv ¼ 100 sec1 (b) Phase margin ’p 458. Solution Using the definition (4.7-3) of the velocity constant Kv yields Kv ¼ lim½sGðsÞ ¼ K s!0
Hence, K ¼ 100 sec1 . The transfer function Gc ðsÞ of the phase lag-lead controller is given by Eq. (9.412), i.e., by the equation 1 þ bT2 s 1 þ aT1 s with ab ¼ 1 Gc ðsÞ ¼ ¼ G1 ðsÞG2 ðsÞ; 1 þ T2 s 1 þ T1 s The determination of Gc ðsÞ will be done in two steps. First, we determine the parameters of G1 ðsÞ and, secondly, the parameters of G2 ðsÞ, as follows. Step 1 Determination of G1 ðsÞ. We draw the Bode diagrams of Gð j!Þ (Figure 9.54). The critical frequency ! 0 is ! 0 ¼ 14 rad/sec. Assume that we want to move ! 0 from
Classical Control Design Methods
417
Figure 9.54 The Bode diagrams of the magnitude and phase plots of the uncompensated and the compensated system of Example 9.9.1. ! 0 ¼ 14 rad/sec to the new position ! 0 ¼ 5 rad/sec using only G1 ðsÞ. Since 20 log jGð j5Þj ¼ 20 dB and using Eq. (9.8-1), the constant b is given by b ¼ 1020 ¼ 0:1 20
To find T2 , we use Eq. (9.8-2), in which case we have 1 5 ¼ bT2 10 and, thus, T2 ¼ 20. Hence G1 ðsÞ has the form G1 ðsÞ ¼
1 þ 2s 1 þ 20s
Step 2 Determination of G2 ðsÞ. The parameter a is calculated by the constraint relation ab ¼ 1, which yields a ¼ 10. The maximum angle which corresponds to a ¼ 10 is found by the relation sin ’m ¼
a1 9 ¼ a þ 1 11
418
Chapter 9
which yields ’m ¼ 54:98. The frequency !m is calculated by the relation 20 log jGð j!m Þ ¼ 0:5½20 log a ¼ 10 dB. From the diagram 20 log jGð j!Þj of Figure 9.54 we get that !m ¼ 9 rad/sec. Finally, from relation (9.7-2), the parameter T1 may be determined as follows: T1 ¼
1 1 1 pffiffiffi ¼ pffiffiffiffiffi ¼ !m a 9 10 28:46
Therefore, the transfer function G2 ðsÞ has the form G2 ðsÞ ¼
1 þ 0:35s 1 þ 0:035s
Finally, Gc ðsÞ has the form 1 þ 0:35s 1 þ 2s Gc ðsÞ ¼ G1 ðsÞG2 ðsÞ ¼ 1 þ 0:035s 1 þ 20s The open-loop transfer function of the compensated system becomes Gc ðsÞGðsÞ ¼
2500ðs þ 2:86Þðs þ 0:5Þ sðs þ 10Þðs þ 2:5Þðs þ 28:6Þðs þ 0:05Þ
In Figure 9.54 one can observe that the phase margin of the compensated system is about 508. Hence, both specifications of the problem are satisfied. 9.10
DESIGN WITH CLASSICAL OPTIMAL CONTROL METHODS
Generally speaking, the ‘‘classical’’ optimal control approach aims to determine the parameters of the controller such as to minimize a specific cost function for a particular type of input signal. More specifically, the classical optimal control problem is formulated as follows. Given a linear time-invariant SISO system, described by the transfer function GðsÞ, apply output feedback as shown in Figure 9.55. The cost function J may have several forms. In this section we consider the two cost functions Je and Ju , where ð1 Je ¼ e2 ðtÞ dt ð9:10-1Þ 0
ð1 Ju ¼
u2 ðtÞ dt 0
Figure 9.55
Block diagram of classical optimal control system.
ð9:10-2Þ
Classical Control Design Methods
419
The cost function Je is called the integral square error (ISE) and expresses the specifications of the closed-loop system that refer to features such as overshoot, rise time, and relative stability. The cost function Ju is called the integral square effort and expresses the energy that is consumed by the control signal in performing the specified control action. The control design problem considered in this chapter is to find the appropriate parameters of the controller Gc ðsÞ in Figure 9.55 such that one of the two cost functions, Je or Ju , or a combination of both, is a minimum. There are two general categories of classical optimal control problems: the case where the structure of the controller is free and the case where the structure of the controller is fixed. Both categories are studied in the material that follows. It is noted that in Chap. 11 we present an introduction to ‘‘modern’’ optimal control approach. In this case, the system under control is described in state space and the derivation of the optimal controller is based on very advanced mathematical techniques, such as the calculus of variations, the maximum principle, and the principle of optimality. Before we present these modern techniques, we thought that it is worthwhile to present in this chapter, in conjunction with other classical control techniques that we have already presented, the ‘‘classical’’ optimal control techniques that were founded before the modern theories of optimal and stochastic control appeared. The classical control methods that we are about to present are useful in practice and are helpful in understanding the modern optimal control methods that follow in Chap. 11. 9.10.1 Free Structure Controllers In this case there are no restrictions on the form of the type of the controller. More specifically, the design problem for the present case is the following: find a controller which minimizes Je (or Ju Þ, withJu ¼ K (or Je ¼ K), where K is a constant. Assume that we wish to minimize Je , while Ju ¼ K. To solve the problem, we make use of the Lagrange multiplier method. This method begins by expressing Je and Ju as a single cost criterion, as follows J ¼ Je þ Ju
ð9:10-3Þ
where is the Lagrange multiplier. The minimization of J will be done in the sdomain by using Parseval’s theorem. Parseval’s theorem relates a function f ðtÞ described in the time domain with its complex frequency counterpart FðsÞ, as follows: ð1 ð 1 j1 2 f ðtÞ dt ¼ FðsÞFðsÞ ds ð9:10-4Þ 2j j1 0 where FðsÞ is the Laplace transformation of f ðtÞ. The theorem is valid under the condition that FðsÞ has all its poles in the left-hand side of the complex plane. Using Eqs (9.10-1) and (9.10-2), the cost function (9.10-3) may be written as ð1 ½e2 ðtÞ þ u2 ðtÞ dt ð9:10-5Þ J ¼ Je þ Ju ¼ 0
If we apply Parseval’s theorem (9.10-4) and (9.10-5), the cost function J becomes
420
Chapter 9
1 J¼ 2j
ð j1 j1
½EðsÞEðsÞ þ UðsÞUðsÞ ds
ð9:10-6Þ
From Figure 9.55, we have EðsÞ ¼ RðsÞ GðsÞUðsÞ
ð9:10-7Þ
Hence, the cost function J takes on the form ð 1 j1 ½RðsÞ GðsÞUðsÞ½RðsÞ GðsÞUðsÞ þ UðsÞUðsÞ ds J¼ 2j j1 ð9:10-8Þ The cost function J is a function of UðsÞ and . To study the maxima and minima of J we apply the method of calculus of variations (see also Subsec. 11.2.1). To this end, assume that UðsÞ is a rational function of s, which is given by the equation UðsÞ ¼ U^ ðsÞ þ "R1 ðsÞ ¼ U^ ðsÞ þ UðsÞ
ð9:10-9Þ
where U^ ðsÞ is the optimal control signal sought, " is a constant, R1 ðsÞ is any rational function whose poles lie in the left-half complex plane, and UðsÞ is the change of UðsÞ about the optimal control signal U^ ðsÞ. If we substitute Eq. (9.10-9) into Eq. (9.10-8), and after some appropriate grouping, we have J ¼ Je þ Ju ¼ J1 þ J2 þ J3 þ J4 where 1 J1 ¼ 2j 1 J2 ¼ 2j 1 J3 ¼ 2j 1 J4 ¼ 2j
ð j1 j1
ð j1
j1
ð j1
j1
ð j1
j1
ð9:10-10Þ
^ þ GGU^ U ^ ds ½RR RGU^ RGU^ þ U^ U
ð9:10-11aÞ
^ GR þ GGU^ "R ds ½U 1
ð9:10-11bÞ
½U^ GR þ GGU^ "R1 ds
ð9:10-11cÞ
½ þ GG"2 R1 R1 ds
ð9:10-11dÞ
where, for simplicity, we use G instead of GðsÞ, G instead of GðsÞ, U instead of UðsÞ, U instead of UðsÞ, etc. Next, we calculate the linear part J of the first differential of J. We observe the following with regard to the factor "R1 : the factor "R1 does not appear in J1 , it appears to the first power in J2 and J3 , and it appears to higher (second) power in J4 . Furthermore, from Eqs (9.10-11b and c) we have that J2 ¼ J3 , which can be easily proven if we substitute s by s and vice versa. Consequently, J becomes ð 2 j1 ½U^ GR þ GGU^ "R1 ds ð9:10-12Þ J ¼ J2 þ J3 ¼ 2j j1 A necessary condition for J to be a minimum for UðsÞ ¼ U^ ðsÞ is that J ¼ 0
ð9:10-13Þ
Classical Control Design Methods
421
For Eq. (9.10-13) to be valid for every "R1 , the function XðsÞ, where XðsÞ ¼ U^ GR þ GGU^ ¼ ½ þ GGU^ GR must satisfy the following condition: ð 2 j1 J ¼ XðsÞ"R1 ds ¼ 0 2j j1
ð9:10-14Þ
ð9:10-15Þ
We assume that the control signal uðtÞ is bounded. Then, it follows that the function R1 ðsÞ has all its poles in the left-half complex plane. This means that R1 ¼ R1 ðsÞ will have all its poles in the right-half complex plane. A sufficient condition for J ¼ 0, independently of R1 , is that the function XðsÞ has all its poles in the righthalf complex plane and that the integration should be performed around the left-half complex plane. Indeed, in this case, if we integrate going from j1 to j1 and passing only through the left-half complex plane, the integral in Eq. (9.10-15) will become zero. Next, define YðsÞYðsÞ ¼ YY ¼ þ GG
ð9:10-16Þ
Since the function þ GG is symmetrical about the j!-axis, it follows that the function YðsÞ has poles and zeros only in the left-half complex plane, while YðsÞ has poles and zeros only on the right-half complex plane. We can express these remarks by the following definitions: Y ¼ ½ þ GGþ
ð9:10-17aÞ
Y ¼ ½ þ GG
ð9:10-17bÞ
The factorization of the function þ GG in the sense of definitions (9.10-17a and b) is called spectral factorization. Hence, XðsÞ may be written as X ¼ YY U^ GR or
or
X GR ¼ Y U^ Y Y
X GR GR ^ ¼ YU þ Y Y Y þ
where
GR Y GR Y
¼
the part of the partial fraction expansion of GR=Y whose poles lie in left-half complex plane
¼
the part of the partial fraction expansion of GR=Y whose poles lie in right-half complex plane
þ
ð9:10-18Þ
The left-hand side of Eq. (9.10-18) involves terms whose poles lie only in the righthalf complex plane, while the right-hand side involves terms whose poles lie only in the left-half complex plane. Hence, in order for Eq. (9.10-18) to hold, both sides must be equal to zero. This yields 1 GR ^ U¼ ð9:10-19Þ Y Y þ
422
Chapter 9
Equation (9.10-19) is the optimal control signal. However, this signal is a function of the parameter . The value of the parameter can be found from the constraint Ju ¼ K, i.e., from the equation ð j1
1 Ju ¼ 2j
U^ ðsÞU^ ðsÞ dsÞ ¼ K
ð9:10-20Þ
j1
To facilitate the calculations of the integral (9.10-20), define 1 In ¼ 2j
ð j1
1 MðsÞMðsÞ ds ¼ 2j j1
ð j1 j1
cðsÞcðsÞ ds dðsÞdðsÞ
where cðsÞ ¼ cn1 sn1 þ þ c1 s þ c0 dðsÞ ¼ dn s þ dn1 s n
n1
and
þ þ d1 s þ d0
The integrals I1 , I2 , I3 , and I4 , are given in Table 9.3. To derive the general expression of In is a formidable task. The transfer function HðsÞ of the optimal closed-loop system is given by HðsÞ ¼
YðsÞ GðsÞ ^ ¼ U ðsÞ RðsÞ RðsÞ
ð9:10-21Þ
Using Eq. (9.10-21) we can determine the transfer function Gc ðsÞ of the optimal controller. Indeed, if for example the closed-loop system is as shown in Figure 9.56, the optimal controller has the form 1 HðsÞ Gc ðsÞ ¼ GðsÞ 1 HðsÞ
Table 9.3
The Integrals I1 , I2 , I3 , and I4
I1 ¼
c20 2d0 d1
I2 ¼
c21 d0 þ c20 d2 2d0 d1 d2
I3 ¼
c21 d0 d1 þ ðc21 2c0 c2 Þd0 d3 þ c21 d2 d3 2d0 d3 ðd0 d3 þ d1 d2 Þ
I4 ¼
c23 ðd02 d3 þ d0 d1 d2 Þ þ ðc22 2c1 c3 Þd0 d1 d4 2d0 d3 ðd0 d32 d12 d42 þ d1 d2 d3 Þ
þ
ðc21 2c0 c2 Þd0 d3 d4 þ c20 ðd1 d42 þ d2 d3 d4 Þ 2d0 d3 ðd0 d32 d12 d42 þ d1 d2 d3 Þ
ð9:10-22Þ
Classical Control Design Methods
Figure 9.56
423
Block diagram with optimal controller.
Remark 9.10.1 The results of the present subsection may also be applied for the case of MIMO systems. However, this extension involves great difficulties, particularly in dealing with the problem of the spectral factorization. Example 9.10.1 Consider the closed-loop system of Figure 9.56, where GðsÞ ¼ 1=s2 and RðsÞ ¼ 1=s. Find the transfer function Gc ðsÞ of the optimal controller such that Je ¼ minimum and Ju 1. Solution We have 1 1 s4 þ 1 þ GG ¼ þ 2 ¼ s ðsÞ2 s4 To factorize the function þ GG, we assume that the output YðsÞ has the form YðsÞ ¼ ½a2 s2 þ a1 s þ a0 =s2 . Then " #" # a2 s 2 þ a1 s þ a0 a2 s 2 a1 s þ a0 a2 s4 ða21 2a0 a2 Þs2 þ a20 YðsÞYðsÞ ¼ ¼ 2 2 2 s s s4 From Eq. (9.10-16), we have s4 þ 1 ¼ a22 s4 ða21 2a0 a2 Þs2 þ a20 Equating the coefficients of like powers of s of both sides in the above equation, we pffiffiffi obtain ¼ a22 , a21 2a0 a2 ¼ 0 and a20 ¼ 1. We finally obtain a0 ¼ 1, a1 ¼ 2 , and a2 ¼ 2 , where ¼ 4 . Hence, pffiffiffi pffiffiffi
2 s2 þ 2 s þ 1
2 s2 2 s þ 1 and YðsÞ ¼ YðsÞ ¼ s2 s2 We also have
1 1 GR 1 s2 s ¼ 2 2 pffiffiffi ¼ 2 2 pffiffiffi Y
s 2 s þ 1 s½ s 2 s þ 1 s2
Therefore
424
Chapter 9
GR 1 ¼ Y þ s where we have considered that the pole s ¼ 0 lies in the left-half complex plane. Using Eq. (9.10-19), we obtain s U^ ðsÞ ¼ 2 2 pffiffiffi
s þ 2 s þ 1 Therefore, we have determined the optimal control signal U^ ðsÞ, as a function of ¼ 4 . To find , we use Eq. (9.10-20) with K ¼ 1. This yields ð 1 j1 s s p ffiffi ffi p ffiffi ffi ds Ju ¼ 2j j1 2 s2 þ 2 s þ 1 2 s2 2 s þ 1 ¼
c21 d0 þ c20 d2 1 ¼ pffiffiffi 3 1 2d0 d1 d2 2 2
where
1
¼ pffiffiffi 2 2
1=3
;
1 4=3 ¼ pffiffiffi ; 2 2
and
UðsÞ ¼
2 s þ 2s þ 2 2
where for the calculation of Ju use was made of Table 9.3. Finally, the transfer function of Gc ðsÞ of the optimal controller has the form 3 2 1 ^ U ðsÞ 1 HðsÞ s 2s 7 6 pffiffiffi ¼ ¼ ¼ s2 4 s Gc ðsÞ ¼ 1 ^ 5 2 s þ 2 s þ 2 GðsÞ 1 HðsÞ 1 U ðsÞ s 9.10.2
Fixed Structure Controllers
In this case, the transfer function Gc ðsÞ of the controller has a preassigned fixed structure. For example, for the closed-loop system of Figure 9.56, the following specific form for Gc ðsÞ may be assigned: Gc ðsÞ ¼
bm sm þ bm1 sm1 þ þ b1 s þ b0 ; sn þ an1 sn1 þ þ a1 s þ a0
mn
ð9:10-23Þ
The problem here is to find the appropriate values of the parameters a0 ; a1 ; . . . ; an1 ; b0 ; b1 ; . . . ; bm of Gc ðsÞ which minimize a cost function J. This method is called the parameter optimization method and is actually a minimization problem of a function involving many variables. To illustrate the method, three examples are presented which show the procedure involved. The last example is of practical interest because it refers to the optimal control of a position control system. Example 9.10.2 Consider the closed-loop system of Figure 9.57. Find the value of K such that the cost function J ¼ Je þ Ju is a minimum when rðtÞ ¼ 1. Solution The error EðsÞ and the signal UðsÞ are given by
Classical Control Design Methods
Figure 9.57
EðsÞ ¼
425
Block diagram of Example 9.10.2.
1 sþK
UðsÞ ¼ KEðsÞ ¼
and
K sþK
Therefore J ¼ Je þ Ju ¼ ¼
1 2j
ð j1 j1
EðsÞEðsÞ ds þ
K2 2j
ð j1 EðsÞEðsÞ ds j1
1 K2 1 K þ þ ¼ 2K 2 2K 2K
The value of K which minimizes J can be found using well-known techniques. For example, take the partial derivative of J with respect to K to yield @J 1 ¼ 2þ ¼0 @K 2 2K Solving the above equation yields that J is minimum when K ¼ K^ ¼ 1=2 . Example 9.10.3 Consider the block diagram of Figure 9.58. The transfer functions GðsÞ and Gc ðsÞ are given by GðsÞ ¼
K s2 ðTs þ 1Þ
and
Gc ðsÞ ¼ 1 þ Kd s
Find the value of the constant Kd which minimizes Je when rðtÞ ¼ 1. Solution The error EðsÞ is given by EðsÞ ¼ RðsÞ YðsÞ Also, we have that
Figure 9.58
Block diagram of Example 9.10.3.
426
Chapter 9
YðsÞ ¼
GðsÞ RðsÞ 1 þ GðsÞGc ðsÞ
Therefore EðsÞ ¼ RðsÞ YðsÞ ¼
1 þ GðsÞGc ðsÞ GðsÞ RðsÞ 1 þ GðsÞGc ðsÞ
Substitute the expressions of GðsÞ and of Gc ðsÞ in the above relation to yield EðsÞ ¼
Ts2 þ s þ KKd Ts3 þ s2 þ KKd s þ K
Using definition (9.10-1) and the Parseval’s theorem, the cost function Je becomes ð1 ð 1 j1 1 1 2 e ðtÞ dt ¼ EðsÞEðsÞ ds ¼ Kd þ Je ¼ 2j j1 2 KðKd TÞ 0 where use was made of Table 9.3. The value Kd for which Je is minimum can be found using well-known techniques. For example, take the partial derivative of Je with respect to Kd to yield @Je 1 1 ¼ 1 ¼0 @Kd 2 KðKd TÞ2 The above equation gives Kd ¼ K^ d ¼ T K 1=2 It is noted that the optimal value K^ d of Kd , as K ! 1, becomes K^ d ¼ T. In this case Gc ðsÞ becomes Gc ðsÞ ¼ 1 þ Ts. In other words, the zero of Gc ðsÞ coincides with one of the poles of GðsÞ. As a result, the closed-loop system is of second order. Example 9.10.4 Consider the position control system described in Subsec. 3.13.2 (Figure 3.51). Find the values of the unspecified parameters of the closed-loop system such as to minimize the cost function ð1 J¼ ½e ðtÞ2 dt 0
For simplicity, let La ’ 0 and Kp ¼ 1. Solution As we have already shown in subsec. 3.13.2, when La ’ 0 and Kp ¼ 1, then the block diagram 3.51c of the closed-loop system is simplified as shown in Figure 9.59. The forward-path transfer function GðsÞ reduces to K ; As þ Bs KK B ¼ B m þ i b Ra
GðsÞ ¼
2
where
K¼
Ka Ki N ; Ra
A ¼ Jm ;
and
Let the input r ðtÞ of the system be the unit step function, i.e., let r ðtÞ ¼ 1. In GðsÞ, all the unspecified parameters K, A, and B of the system are to be chosen so as to
Classical Control Design Methods
Figure 9.59
427
Simplified block diagram of the servomechanism.
minimize J. To determine the values of K, A, and B which minimize J, use the Parseval’s theorem given by relation (9.10-4). For this example, we have ð1 ð 1 j1 2 ½e ðtÞ dt ¼ ðsÞe ðsÞ ds 2j j1 e 0 It can be easily shown that " # As2 þ Bs e ðsÞ ¼ r ðsÞ As2 þ Bs þ K Since r ðtÞ ¼ 1, or r ðsÞ ¼ 1=s, the above equation becomes e ðsÞ ¼
As þ B As þ Bs þ K 2
Since e ðsÞ is of the form cðsÞ=dðsÞ, where cðsÞ ¼ c0 þ c1 s and dðsÞ ¼ d0 þ d1 s þ d2 s2 , the calculation of the Parseval’s integral can be done by using Table 9.3, where c0 ¼ B, c1 ¼ A, d0 ¼ K, d1 ¼ B and d2 ¼ A. using Table 9.3 yields I2 ¼
c21 d0 þ c20 d2 A2 K þ B2 A AK þ B2 ¼ ¼ 2KBA 2d0 d1 d2 2KB
Hence ð1 ½e ðtÞ2 dt ¼
J¼ 0
AK þ B2 2KB
Clearly, the above cost function J is an analytical expression of the cost function J in terms of the parameters K, A, and B of the closed-loop system. We will further investigate the above expression for J in terms of K, A, and B, wherein we distinguish the following four interesting cases: Case 1 Let K and B be constants. Then, for J to be minimum we must have A ¼ 0, in which case J ¼ B=2K. Case 2 A more realistic approach is to assume that K and A are constant. Then, J becomes pffiffiffiffiffiffiffi maximum with respect to B when @J=@B ¼ 0, which gives B ¼ KA. Returning to Figure 9.59, we may write the differential equation of the closed-loop system as follows:
428
Chapter 9
d2 y ðtÞ dy ðtÞ þ !2n y ðtÞ ¼ !2n r ðtÞ þ 2!n 2 dt dt where B ¼ pffiffiffiffiffiffiffi 2 KA
and
rffiffiffiffi K !n ¼ A
pffiffiffiffiffiffiffi Clearly, in the present case where B ¼ KA, we have ¼ 0:5. That is, we have the very interesting result that the value of the damping ratio ¼ 0:5. Case 3 Let A and B be constants. The parameter K is strongly influenced by the amplification constant Ka of the amplifier. If we differentiate J with respect to K, then the derivative tends to zero as K ! 1. For K ! 1, we obtain lim J ¼
K!1
A 2B
Case 4 One more useful case is to limit the values of K and B such that KB ¼ C, where C is a cosntant. Then, since K ¼ C=B, the cost function J becomes J¼
ACB1 þ B2 2C
The partial derivative @J=@B is zero when 2B3 ¼ AC ¼ AKB. Hence rffiffiffiffiffiffiffi KA B¼ 2 pffiffiffi In this case the damping ratio ¼ ½2 21 ffi 0:353. Finally, it is noted that by using the above results, one may study other combinations of K, A, and B. 9.11
PROBLEMS
1. For the control system shown in Figure 9.60, solve the design problems given in Table 9.4. Furthermore: (a) draw the Bode and the Nichols diagrams (b) plot the amplitudes M and the step responses of the systems.
Figure 9.60
Classical Control Design Methods
429
Table 9.4 Compensator type
GðsÞ
Design requirements
1
1 sð1 þ 0:2sÞ
Phase-lead controller
Kv ¼ 4 sec1 ’p 408
2
1 sð1 þ 0:1sÞ
Phase-lag controller
Kv ¼ 10 sec1 ’p 408
3
1 s2 ð1 þ 0:2sÞ
Phase-lead controller
Ka ¼ 4 sec2 Mp 2
4
1 ðs þ 0:5Þðs þ 0:1Þðs þ 0:2Þ
Phase-lag controller
Kp ¼ 1 Mp 0:7
2. The closed-loop block diagram for controlling the altitude of a space vehicle is given in Figure 9.61. Determine a phase-lead controller so that for the closedloop system the settling time (2%) is Ts 4 sec and the maximum percent overshoot is less than 20%. 3. Consider the case of controlling the angle of the robot arm shown in Figure 9.62a. Determine a phase-lag network so that Kv ¼ 20 sec1 and ¼ 0:707 for the compensated closed-loop system shown in Figure 9.62b. 4. The open-loop transfer function of a position control servomechanism is given by GðsÞFðsÞ ¼
K sð0:1s þ 1Þð0:2s þ 1Þ
Design a phase lag-lead compensator such that for the compensated closed-loop system the velocity error constant is Kv ¼ 30 sec1 , the phase margin is ’p ffi 508, and the bandwidth BW ffi 12 rad/sec. 5. Consider the orientation control system of a satellite described in Subsec. 3.13.7 and Example 9.6.1, where the controller is a PD controller and Kt ¼ Kb ¼ J ¼ 1. Determine the parameters of the PD controller such that for the closed-loop system ¼ 0:7 and !n ¼ 2.
Figure 9.61
430
Chapter 9
Figure 9.62
6. Consider the position servomechanism shown in Figure 9.63, where a PI controller is used. Determine the parameters of the PI controller for a 25% overshoot. 7. Consider the system shown in Figure 9.64. Determine the parameters of the PI controller, such that the poles of the closed-loop system are 2 and 3. 8. Consider the system shown in Figure 9.65. Determine the parameters of the PID controller, such that the poles of the closed-loop system are 2 þ j, 2 j, and 5. 9. Consider the system shown in Figure 9.66. Using the Ziegler–Nichols stability limit method, determine the parameters of the PID controller in order to achieve an overshoot of 25%.
Figure 9.63
Classical Control Design Methods
431
Figure 9.64
Figure 9.65
Figure 9.66
10. Consider the system with transfer function GðsÞ ¼
1 ðs þ 1Þð0:2s þ 1Þð0:05s þ 1Þð0:01s þ 1Þ
Draw the step response of the system and determine the parameters of the PID controller using the Ziegler–Nichols method. 11. Find an active-circuit realization for each of the controllers found in Problems 4, 5, 6, and 7. 12. Consider the system shown in Figure 9.67, where rðtÞ ¼ 1. Find the transfer functions of the controllers Gc ðsÞ and FðsÞ so that ð1 ð1 2 Je ¼ e ðtÞ dt is minimized, while Ju ¼ u2 ðtÞ dt 2: 0
0
13. The orientation control system of a space telescope is shown in Figure 9.68. Given that RðsÞ ¼ 0:5=s, determine the optimal control signal UðsÞ, the optimal
432
Chapter 9
Figure 9.67
Figure 9.68
Figure 9.69
linear controller, and the optimal closed-loop Ð1 Ð 1 transfer function HðsÞ, so that Je ðtÞ ¼ 0 e2 ðtÞ dt is minimized, while Ju ¼ 0 u2 ðtÞ 2:5. 14. For the system Ð 1shown in Figure 9.69, find the value of the parameter T for which the cos Je ¼ 0 e2 ðtÞ dt is minimized for rðtÞ ¼ 1. BIBLIOGRAPHY Books 1. 2. 3. 4.
K Astrom, T Hagglund. PID Controllers: Theory, Design, and Tuning. Research Triangle Park, North Carolina: Instrument Society of America, 1995. JJ D’Azzo, CH Houpis. Linear Control System Analysis and Design, Conventional and Modern. New York: McGraw-Hill, 1975. JJ DiStefano III, AR Stubberud, IJ Williams. Feedback and Control Systems. Schaum’s Outline Series. New York: McGraw-Hill, 1967. RC Dorf, RE Bishop. Modern Control Analysis. London: Addison-Wesley, 1995.
Classical Control Design Methods
433
5.
AF D’Souza. Design of Controls Systems. Englewood Cliffs, New Jersey: Prentice hall, 1988. 6. GF Franklin, JD Powell, A Emami-Naeini. Feedback Control of Dynamic Systems. Reading, MA: Addison-Wesley, 1986. 7. GH Hostetter, CJ Savant Jr, RT Stefani. Design of Feedback Control Systems. 2nd ed. New York: Saunders College Publishing, 1989. 8. NS Nise. Control Systems Engineering. New York: Benjamin and Cummings, 1995. 9. K Ogata. Modern Control Systems. London: Prentice Hall, 1997. 10. FG Shinskey. Process Control Systems. New York: McGraw-Hill, 1979.
Articles 11.
JG Ziegler, NB Nichols. Optimum settings for automatic controllers. Trans ASME 64:759–768, 1942.
10 State-Space Design Methods
10.1
INTRODUCTION
This chapter presents an introduction to certain modern state-space control design methods. The specific methods presented are distinguished into two categories: the algebraic control methods and the observer design methods. There are many other interesting modern control design methods presented in the remainder of this book: optimal control (Chap. 11), digital control (Chap. 12), system identification (Chap. 13), adaptive control (Chap. 14), robust control (Chap. 15), and fuzzy control (Chap. 16). All these modern control methods are of paramount theoretical and practical importance to the control engineer. It should be mentioned that there are several control design methods—such as geometrical control, hierarchical control, and neural control—that are not presented here, since they are beyond the scope of this book. Algebraic control refers to a particular category of modern control design problems wherein the controller has a prespecified structure. In this case, the design problem reduces to that of determining the controller parameters such that certain closed-loop requirements are met. This is not achieved via minimization of some cost functions (as is done, for example, in optimal control in Chap. 11), but via the solution of algebraic equations. It is for this reason that these techniques are called algebraic control design techniques. These algebraic techniques are used to solve many interesting practical control problems, such as pole placement, input–output decoupling, and exact model matching. These three problems are studied in Secs 10.3, 10.4, and 10.5, respectively. In Sec. 10.2 an overview of the structure of state and output feedback laws is given, which are subsequently used for the study of the three aforementioned algebraic control problems. State observers are used in order to produce a good estimate of the state vector xðtÞ. It is well known that, in practice, most often not all state variables of a system are accessible to measurement. This obstacle can be circumvented by the use of state observers which yield a good estimate x^ ðtÞ of the real state vector xðtÞ, provided that a mathematical model of the system is available. Estimating x^ ðtÞ makes it possible to use state feedback techniques to solve many important control problems, such as pole assignment, input–output decoupling, and model matching, presented in Secs 435
436
Chapter 10
10.3–10.5; optimal regulator and optimal servomechanism, presented in Chap. 11; and many others. 10.2
LINEAR STATE AND OUTPUT FEEDBACK LAWS
In designing control systems using algebraic techniques, we usually apply linear state or output feedback. 1 State Feedback Controllers Consider the linear time-invariant system x ¼ Ax þ Bu
ð10:2-1aÞ
y ¼ Cx
ð10:2-1bÞ
where x 2 Rn , u 2 Rm , y 2 Rp and the matrices A, B, and C are of appropriate dimensions. Let the controller have the linear state feedback form u ¼ Fx þ Gr
ð10:2-2Þ
where r 2 Rm is a new vector with m inputs and F and G are the unknown controller matrices with dimensions m n and m m , respectively (Figure 10.1). Substituting Eq. (10.2-2) in Eq. (10.2-1) yields the closed-loop system x ¼ ðA þ BFÞx þ BGr
ð10:2-3aÞ
y ¼ Cx
ð10:2-3bÞ
The control problem here is to determine the control law (10.2-2), i.e., to determine the controller matrices F and G, such that the closed-loop system has the desired prespecified characteristics. 2 Output Feedback Controllers Consider the system (10.2-1) and the linear output feedback controller u ¼ Ky þ Nr
Figure 10.1
Closed-loop system with state feedback.
ð10:2-4Þ
State-Space Design Methods
Figure 10.2
437
Closed-loop system with output feedback.
where K and N are the unknown controller matrices with dimensions m p and m m , respectively (Figure 10.2). Substituting Eq. (10.2-4) in Eq. (10.2-1) yields the closed-loop system x ¼ ðA þ BKCÞx þ BNr ð10:2-5aÞ y ¼ Cx
ð10:2-5bÞ
The problem here is to determine the control law (10.2-4), i.e., to determine the controller matrices K and N, such that the closed-loop system (10.2-5) has the desired prespecified characteristics. By inspection, we observe that the controller matrices (F; GÞ and ðK; NÞ of the foregoing controller design problems via state or output feedback, respectively, are related via the following equations F ¼ KC
ð10:2-6aÞ
G¼N
ð10:2-6bÞ
In fact, if Eqs (10.2-6a and b) hold true, the closed-loop systems (10.2-4) and (10.2-5) are identical. This shows that if the problem via state feedback has a solution, Eqs (10.2-6a and b) may facilitate the solution of the problem via output feedback. In this latter case, the solution procedure will be simple since Eqs (10.2-6a and b) are linear in K and N. The main differences between the state and the output feedback methods are the following. The state feedback method has the advantage over the output feedback method in that it has greater degrees of freedom in the controller parameters. This is true since the matrix F has nm arbitrary elements, while K has mp < mn arbitrary elements. However, the output feedback method is superior to the state feedback method from the practical point of view, because the output vector yðtÞ is known and measurable. On the contrary, it is almost always difficult, if not impossible, to measure the entire state vector xðtÞ, in which case we are forced to use a special type of system, called the state observer, for the estimation of the vector xðtÞ (see Sec. 10.6). In the sequel, the problem of determining the matrices F and G (or K and N) is considered for the following three specific problems: pole placement, input–output decoupling, and exact model matching. These three problems have been chosen because they are very useful in practice.
438
Chapter 10
10.3
POLE PLACEMENT
10.3.1
Pole Placement via State Feedback
Consider the linear, time-invariant system ¼ AxðtÞ þ BuðtÞ xðtÞ
ð10:3-1Þ
where we assume that all states are accessible and known. To this system we apply a linear state feedback control law of the form uðtÞ ¼ FxðtÞ
ð10:3-2Þ
Then, the closed-loop system (see Figure 10.3) is given by the homogeneous equation ¼ ðA BFÞxðtÞ xðtÞ
ð10:3-3Þ
It is remarked that the feedback law uðtÞ ¼ FxðtÞ is used rather than the feedback law uðtÞ ¼ FxðtÞ. This difference in sign is chosen to facilitate the observer design problem presented in Sec. 10.6. Here, the design problem is to find the appropriate controller matrix F so as to improve the performance of the closed-loop system (10.3-3). One such method of improving the performance of (10.3-3) is that of pole placement. The pole-placement method consists in finding a particular matrix F, such that the poles of the closedloop system (10.3-3) take on desirable preassigned values. Using this method, the behavior of the open-loop system may be improved significantly. For example, the method can stabilize an unstable system, increase or decrease the speed of response, widen or narrow the system’s bandwidth, increase or decrease the steady-state error, etc. For these reasons, improving the system performance via the pole-placement method is widely used in practice. The pole placement or eigenvalue assignment problem can be defined as follows: let 1 ; 2 ; . . . ; n be the eigenvalues of the matrix A of the open-loop system (10.3-1) and ^1 ; ^2 ; . . . ; ^n be the desired eigenvalues of the matrix A BF of the closed-loop system (10.3-3), where all complex eigenvalues exist in complex conjugate pairs. Also, let pðsÞ and p^ðsÞ be the respective characteirstic polynomials, i.e., let
Figure 10.3
Closed-loop system with a linear state feedback law.
State-Space Design Methods
pðsÞ ¼
439
n Y ðs i Þ ¼ jsI Aj ¼ sn þ a1 sn1 þ þ an1 s þ an
ð10:3-4Þ
i¼1
p^ ðsÞ ¼
n Y ðs ^i Þ ¼ jsI A þ BFj ¼ sn þ a^ 1 sn1 þ þ a^n1 s þ a^ n
ð10:3-5Þ
i¼1
Find a matrix F so that Eq. (10.3-5) is satisfied. The pole-placement problem has attracted considerable attention for many years. The first significant results were established by Wonham in the late 1960s and are given by the following theorem [25]. Theorem 10.3.1 There exists a state feedback matrix F which assigns to the matrix A BF of the closed-loop system any arbitrary eigenvalues ^1 ; ^2 ; . . . ; ^n , if and only if the state vector of the open-loop system (10.3-1) is controllable, i.e., if and only if rankS ¼ n;
where
. . . . S ¼ ½B .. AB .. A2 B .. .. An1 B
ð10:3-6Þ
where all complex eigenvalues of the set f^1 ; . . . ; ^n g appear in conjugate pairs. According to this theorem, in cases where the open-loop system (10.3-1) is not controllable, at least one eigenvalue of the matrix A remains invariant under the state feedback law (10.3-2). In such cases, in order to assign all eigenvalues, one must search for an appropriate dynamic controller wherein the feedback law (10.3-2) may involve, not only propontial, but also derivative, integral and other terms (a special category of dynamic controllers are the PID controllers presented in Sec. 9.6). Dynamic controllers have the disadvantage in that they increase the order of the system. Now, consider the case where the system ðA; BÞ is controllable, a fact which guarantees that there exists an F which satisfies the pole-placement problem. Next, we will deal with the problem of determining such a feedback matrix F. For simplicity, we will first study the case of single-input systems, in which case the matrix B reduces to a column vector b and the matrix F reduces to a row vector f T . Equation (10.3-5) then becomes p^ ðsÞ ¼
n Y ðs ^i Þ ¼ jsI A þ bf T j ¼ sn þ a^ 1 sn1 þ þ a^n1 s þ a^ n
ð10:3-7Þ
i¼1
It is remarked that the solution of Eq. (10.3-7) for f is unique. Several methods have been proposed for determining f. We present three wellknown such methods. Method 1. The Base–Gura Formula. One of the most popular pole-placement methods, due to Bass & Gura [3], gives the following simple solution: f ¼ ½WT ST 1 ð^a aÞ where S is the controllability matrix defined in Eq. (10.3-6) and
ð10:3-8Þ
440
Chapter 10
2
1 a1 60 1 6 W ¼ 6 .. .. 4. . 0 0
3 an1 an2 7 7 .. 7; . 5
3 a^ 1 6 a^ 2 7 6 7 a^ ¼ 6 .. 7; 4 . 5
3 a1 6 a2 7 6 7 a ¼ 6 .. 7 4 . 5
2
2
a^ n
1
ð10:3-9Þ
an
Method 2. The Phase Canonical Form Formula. Consider the special case where the system under control is described in its phase-variable canonical form, i.e., A and b have the special forms A and b , where (see Subsec. 5.4.2) 3 2 3 2 0 1 0 0 0 0 1 0 7 607 6 0 7 6 7 6 607 6 0 0 0 0 7 7 6 7 ð10:3-10Þ b ¼6 A ¼ 6 .. .. 7; .. .. 6 ... 7 . 7 . . 6 7 6 . 405 4 0 0 0 1 5 a n
a n1
a n2
a 1
1 .. .. 2 .. Then, it can be easily shown that the matrix S ¼ ½b . A b . A b . is such that the product WT S T reduces to the simple form 3 2 0 0 0 1 60 0 1 07 .. .. 7 WT S T ¼ I~ ¼ 6 4 ... ... . .5
1
0
0
.. n1 b .A
ð10:3-11Þ
0
In this case, the vector f in expression (10.3-8) reduces to f ¼ I~ ð^aaÞ, i.e., it reduces to the following form [22]: 2 3 a^ n an 6 a^ n1 an1 7 7 6 ð10:3-12Þ f ¼ I~ ð^a aÞ ¼ 6 7 .. 5 4 . a^ 1 a1 where use is made of the property ðI~ Þ1 ¼ I~ . It is evident that expression (10.3-12) is extremely simple to apply, provided that the matrix A and the vector b of the system under control are in the phase-variable canonical form (10.3-10). Method 3. The Ackermann’s Formula. Another approach for computing f has been proposed by Ackermann, leading to the following expression [5]: f T ¼ eT S1 p^ ðAÞ
ð10:3-13Þ
where the matrix S is given in Eq. (10.3-6) and e ¼ ð0; 0; . . . ; 0; 1Þ. The matrix polynomial p^ ðAÞ is given by Eq. (10.3-5), wherein the variable s is substituted by the matrix A, i.e., T
p^ ðAÞ ¼ An þ a^ 1 An1 þ þ a^n1 A þ a^ n I
ð10:3-14Þ
In the general case of multi-input systems, the determination of the matrix F is somewhat complicated. A simple approach to the problem is to assume that F has the following outer product form: F ¼ qpT
ð10:3-15Þ
State-Space Design Methods
441
where q and p are n-dimensional vectors. Then, the matrix A BF becomes A BF ¼ A BqpT ¼ A bpT ;
where
b ¼ Bq
ð10:3-16Þ
Therefore, assuming that F has the form (10.3-15), then the multi-input system case is reduced to the single-input case studied previously. In other words, the solution for the vector p is Eq. (10.3-8) or Eq. (10.3-13) and differs only in that the matrix S is now the matrix S~ , having the form . . . . S~ ¼ ½b .. Ab .. A2 b .. ..
ð10:3-17Þ
An1 b
The vector b ¼ Bq involves arbitrary parameters, which are the elements of the arbitrary vector q. These arbitrary parameters can have any value, provided that rank S~ ¼ n. In cases where this condition cannot be satisfied, other approaches for determining F may be found in the literature [22]. Example 10.3.1 Consider a system in the form (10.3-1), where 0 1 0 and b¼ A¼ 1 0 1 Find a vector f such that the closed-loop system has eigenvalues ^1 ¼ 1 and ^2 ¼ 1:5. Solution We have pðsÞ ¼ jsI Aj ¼ s2 þ 1
p^ ðsÞ ¼ ðs ^1 Þðs ^2 Þ ¼ s2 þ 2:5s þ 1:5
and
Method 1. Here we use Eq. (10.3-8). Equations (10.3-9) and (10.3-6) give . 1 a1 1 0 0 1 ¼ and S ¼ ½b .. Ab ¼ W¼ 0 1 0 1 1 0 Therefore
1 W S ¼ 0 T
T
0 1
0 1
1 0 ¼ 0 1
Hence f ¼ ðWT ST Þ1 ða^ aÞ ¼
0 1
1 0
1 0
and
T 1
ðW S Þ T
0 0 2:5 ¼ 1 1 1:5
1 0
0 1 ¼ 1 0
0:5 2:5 ¼ 2:5 0:5
Method 2. Since the system is in phase-variable canonical form, the vector f can readily be determined by Eq. (10.3-12), as follows: 1:5 1 0:5 a^ 2 a2 f¼f ¼ ¼ ¼ a^ 1 a1 2:5 0 2:5 Method 3. Here we apply Eq. (10.3-13). We have
442
Chapter 10
p^ ðAÞ ¼ A2 þ a^1 A þ a^ 2 I ¼ a2 þ 2:5A þ 1:5I 0 1 2 0 1 1 0 ¼ þ2:5 þ 1:5 1 0 1 0 0 1 0:5 1:5 0 0 2:5 1 0 ¼ þ þ ¼ 2:5 0 1:5 2:5 0 0 1 .. 0 1 1 1 S ¼ ½b . Ab ¼ 1 0 Therefore f T ¼ eT S1 p^ ðAÞ ¼ ½0
1
0 1 1 0
0:5 2:5
2:5 ¼ ½0:5 0:5
2:5
0:5
2:5
Clearly, the resulting three controller vectors derived by the three methods are identical. This is due to the fact that for single-input systems, f is unique. Example 10.3.2 Consider a system 2 0 1 A ¼ 40 0 1 0
in the form (10.3-1), where 3 2 3 0 0 15 and b ¼ 405 0 1
Find a vector f such that the closed-loop system has eigenvalues ^1 ¼ 1, ^2 ¼ 2, and ^3 ¼ 2. Solution We have pðsÞ ¼ jsI Aj ¼ s3 1
and
p^ ðsÞ ¼ ðs ^1 Þðs ^2 Þðs ^3 Þ
¼ s3 þ 5s2 þ 8s þ 4 Method 1. Here, we 2 1 a1 W ¼ 40 1 0 0 Therefore,
make use of 3 2 1 a2 a1 5 ¼ 4 0 1 0
2
1 0
6 WT ST ¼ 4 0 0 2 0 6 T T 1 ðW S Þ ¼ 4 0
32
0
1
76 0 54 0
0
1
0 1
1 7 05
1 0 Hence
0
Eq. (10.3-8). Equations (10.3-9) and (10.3-6) give 3 2 3 0 0 0 0 1 .. .. 2 1 0 5; S ¼ ½b . Ab . A b ¼ 4 0 1 0 5 0 1 1 0 0
0
3
1
0 1
3
2
0
7 6 1 05 ¼ 40 1 0 0
0
1
3
1
7 05
0
0
and
State-Space Design Methods
443
2
0 0 6 T T 1 f ¼ ðW S Þ ð^a aÞ ¼ 4 0 1 1 0 2 3 5 6 7 ¼ 485 5
382 3 2 39 2 1 > 0 0 > < 5 = 7 6 7 6 6 7 05 485 4 05 ¼ 40 > > ; : 0 4 1 1
0 1
32 3 1 5 76 7 0 54 8 5
0
0
5
Method 2. Since the system is in phase-variable canonical form, the vector f can readily be determined by Eq. (10.3-12), as follows: 2 3 2 3 2 3 4þ1 5 a^ 3 a3 f ¼ f ¼ 4 a^ 2 a2 5 ¼ 4 8 þ 0 5 ¼ 4 8 5 5þ0 5 a^ 1 a1 Method 3. Here, we make use of Eq. (10.3-13). We have p^ ðAÞ ¼ A3 þ a^ 1 A2 þ a^ 2 A þ a^ 3 I ¼ A3 þ 5A2 þ 8A þ 4I 33 2 32 2 3 2 2 1 0 1 0 0 1 0 0 1 0 7 6 6 7 7 6 6 ¼ 4 0 0 1 5 þ54 0 0 1 5 þ84 0 0 1 5 þ 44 0
0 1
0
0
2
1 0
1 6 ¼ 40 0 2 5 6 ¼ 45
0 1 0 8 5
8 5 . . S1 ¼ ½b .. Ab ..
0
0 0 3 2 0 0 0 5 0 7 6 7 6 05 þ 45 0 05 þ 40 8 0 5 0 1 3 5 7 85 5 2 3 0 0 1 6 7 A2 b1 ¼ 4 0 1 0 5 1 0 0 3
2
Therefore f T ¼ eT S1 p^ ðAÞ ¼ ½0
1
2
0
0 14 0 1
0 1 0
1
0 8 0 0
0 3
2
4 7 6 85 þ 40 0 0 0
32 1 5 8 0 54 5 5 0 8 5
3 5 8 5 ¼ ½5 5
0 0
3 0 7 05 3
1
7 4 05 0 4
8
5
The resulting three controller vectors derived by the three methods are identical. As mentioned in the previous example, this is becuase, for single-input systems, f is unique. Example 10.3.3 Consider the position control system shown in Figure 10.4. The state variables of the system are as follows. State x1 ¼ y ¼ m is the angular position of the motor axis which is converted into an electrical signal through the use of a potentiometer. State x2 ¼ _m ¼ !m is the angular velocity of the motor which is mea-
444
Chapter 10
Figure 10.4
Position control system of Example 10.3.3. (a) Overall picture of a position control system, with a motor controlled by the stator; (b) schematic diagram of the position control system.
sured by the tachometer. State x3 ¼ if is the current of the stator. To measure if , we insert a small resistor R in series with the inductor. The voltage vR ðtÞ ¼ Rif is fed into an amplifier with gain 1=R, which produces an output if ¼ x3 . Using the state equations (3.12-8) and Figure 3.39 of Chap. 3, we can construct the block diagram for the closed-loop system as shown in Figure 10.5. It is noted that the amplifier with gain Ka , which is inserted between eðtÞ and uðtÞ, is used to amplify the signal eðtÞ, which is usually small. The control of the angular position m is achieved in the following way. The external control signal r is the desired angu-
State-Space Design Methods
445
Figure 10.5 Block diagram of the closed-loop system in Example 10.3.3. (a) Block diagram of the position control system; (b) simplified block diagram for Lf ¼ 0:5, Rf ¼ 2, Jm ¼ 1, Bm ¼ 1, and Km Kf Ia ¼ 2.
lar position y ¼ m of the motor axis. If y 6¼ r , then part of the error eðtÞ is due to the difference y r . This difference is amplified by the amplifier, which subsequently drives the motor, resulting in a rotation of the axis so that the error y r reduces to zero. The problem here is to study the pole placement problem of the closed-loop system via state feedback. Solution The state equations of the closed-loop system in Figure 10.5b are (compare with Eq. (3.12-8))
446
Chapter 10
x ¼ Ax þ bu y ¼ cT x u ¼ Ka ½f T x þ r where 2
3 2 3 y x1 6 7 6 7 x ¼ 4 x2 5 ¼ 4 !m 5;
2
3 f1 6 7 f ¼ 4 f2 5;
if
x3 2 3 0 6 7 b ¼ 4 0 5; 2
2
0 1 6 A ¼ 4 0 1 0
f3
2 3 1 6 7 c ¼ 405 0
0
3 0 7 2 5; 4
where use was made of the definitions Tf1 ¼ Rf =Lf ¼ 4 and Tm1 ¼ Bm =Jm ¼ 1. The controllability matrix of the open-loop system is 2 3 0 0 4 .. .. 2 S ¼ ½b . Ab . A b ¼ 4 0 4 20 5 2 8 32 The determinant of the matrix S is jSj ¼ 32 6¼ 0. Consequently, we may arbitrarily shift all poles of the closed-loop system of Figure 10.5b via state feedback. The characteristic polynomials pðsÞ and p^ ðsÞ of the open-loop and closed-loop systems are pðsÞ ¼ jsI Aj ¼ s3 þ 5s2 þ 4s ¼ sðs þ 1Þðs þ 4Þ p^ ðsÞ ¼ jI A þ Ka bf T j ¼ s3 þ 2 s2 þ 2 s þ 0 ¼ ðs ^1 Þðs ^2 Þðs ^3 Þ where ^1 , ^2 , and ^3 are the desired poles of the closed-loop system. To determine the vector f we use formula (10.3-8). The matrix W has the form 2 3 2 3 0 0 14 1 5 4 W ¼ 40 1 55 and ½WT ST 1 ¼ 4 14 14 0 5 1 0 0 1 2 0 0 Hence Ka f ¼ ½WT ST 1 ð^a aÞ
or
f¼
1 ½WT ST 1 ð^a aÞ Ka
Finally 3 1 7 2 3 6 4Ka 0 7 6 f1 7 6 1 1 7 6 7 6 ð5 2 Þ þ ð4 1 Þ 7 f ¼ 4 f2 5 ¼ 6 7 6 4Ka 4Ka 7 6 f3 5 4 1 ð5 2 Þ 2Ka 2
State-Space Design Methods
447
10.3.2 Pole Placement via Output Feedback For the case of pole placement via output feedback wherein u ¼ Ky, a theorem similar to the Theorem 10.3.1 has not yet been proven. The determination of the output feedback matrix K is, in general, a very difficult task. A method for determining the matrix K, which is closely related to the method of determining the matrix F presented earlier, is based on Eq. (10.2-6a), namely on the equation F ¼ KC
ð10:3-18Þ
This method starts with the determination of the matrix F and in the sequel the matrix K is determined by using Eq. (10.3-18). It is fairly easy to determine the matrix K from Eq. (10.3-18) since this equation is linear in K. A more general method to determine matrix K is given in [16]. Note that Eq. (10.3-18) is only a sufficient condition. That is, if Eq. (10.3-18) does not have a solution for K, it does not follow that pole placement by output feedback is impossible. Example 10.3.4 Consider the multi-input–multi-output (MIMO) system of the form x ¼ Ax þ Bu;
y ¼ Cx
where 2
0 1 A ¼ 4 2 3 5 1
3 0 0 5; 3
2
0 B ¼ 41 0
3 0 3 5; 1
0 C¼ 7
0 7 9 0
Find an output feedback matrix K such that the poles of the closed-loop system are 3, 3, and 4. Solution First, we determine a state feedback matrix F which satisifes the problem. Using the techniques of Subsec. 10.3.1, we obtain the following matrix F:
21 7
7 9 F¼ 0 0
Now, consider the equation F ¼ KC and investigate if the above matrix F is sufficient for the determination of the matrix K. Simple algebraic calculations lead to the conclusion that F ¼ KC has a solution for K having the following form: K¼
3 1
1 0
Checking the results, we have
448
Chapter 10
2
0
6 A BKC ¼ 4 2 5 2 0 6 ¼ 4 9 5
1
0
3
2
0
7 6 3 05 þ 41 1 3 0 3 1 0 7 6 05 1 4
and hence
s 1 jsI A þ BKCj ¼ 9 s þ 6 5 1
3 0 7 3 35 1 1
1 0
0 7
0 9
7 0
0 0 ¼ ðs þ 4Þðs þ 3Þ2 s þ 4
Example 10.3.5 Consider the position control system of Example 10.3.3. To this system apply output feedback for pole placement. Solution In practice, the output variable y is usually the output position y . Thus y ¼ y ¼ m ¼ x1 ¼ cT x, where cT ¼ ð1 0 0Þ. Hence, the output feedback law here is u ¼ Ka ½ky þ r ¼ Ka ½kcT x þ r , where k is the output feedback controller or gain. The characteristic polynomial p^ ðsÞ of the closed-loop system then becomes p^ ðsÞ ¼ jsI A þ Ka bkcT j ¼ s3 þ 5s2 þ 4s þ 4Ka k By using one of the algebraic stability criteria of Chap. 6 we conclude that the closedloop system is stable when 0 < Ka k < 5. By using the material of Chap. 7, one may draw the root-locus diagram for p^ðsÞ, thus revealing the regions of the root locus where the closed-loop system is stable. It must be clear that for single-output systems, using output feedback, we may be able to make the closed-loop system stable, but we cannot shift the poles to any arbitrary positions, as in the case of state feedback. 10.4
INPUT–OUTPUT DECOUPLING
The problem of input–output decoupling of a system may be stated as follows. Consider the system (10.2-1) and assume that it has the same number of inputs and outputs, i.e., assume that p ¼ m. Determine a pair of matrices F and G of the state feedback law (10.2-2) (or a pair of matrices K and N of the output feedback law (10.2-4)) such that every input of the closed-loop system (10.2-3) (or of the closedloop system (10.2-5)) influences only one of the systems outputs, and vice-versa, every output of the closed-loop system is influenced by only one of its inputs. More precisely, in an input–output decoupled system the following relation must hold yi ¼ f ðri Þ;
i ¼ 1; 2; . . . ; m
ð10:4-1Þ
The transfer function matrix HðsÞ of the closed-loop system (10.2-3) is given by
State-Space Design Methods
449
HðsÞ ¼ CðsI A BFÞ1 BG
ð10:4-2Þ
^ ðsÞ of the closed-loop system (10.2-5) is given by and the transfer function matrix H ^ ðsÞ ¼ CðsI A BKCÞ1 BN H
ð10:4-3Þ
^ ðsÞRðsÞÞ it follows that a definition, equivalent to Since YðsÞ ¼ HðsÞRðsÞ (or YðsÞ ¼ H the foregoing definition of the input–output decoupling problem, is the following: determine a pair of matrices F and G (or a pair of matrices K and N) such that the ^ ðsÞ) is regular and diagonal. In fact, if HðsÞ is transfer function matrix HðsÞ (or H regular and diagonal, that is if HðsÞ has the form 3 2 h11 ðsÞ 0 0 6 0 0 7 h22 ðsÞ 6 7 ð10:4-4Þ HðsÞ ¼ 6 . .. 7 . . .. .. 4 .. . 5 0
0
hmm ðsÞ
with jHðsÞj 6¼ 0, then equation YðsÞ ¼ HðsÞRðsÞ may be written as follows: yi ðsÞ ¼ hii ðsÞri ðsÞ;
i ¼ 1; 2; . . . ; m
ð10:4-5Þ
Equation (10.4-5) is equivalent to Eq. (10.4-1). A similar definition may be given for ^ ðsÞ. the matrix transfer function H The basic motivation for input–output decoupling of a system is that by making each output of the system depend only upon one input and vice versa, we convert a MIMO system to m single-input–single-output (SISO) systems. This fact significantly simplifies and facilitates the control of the closed-loop system, since one has to deal with m scalar systems rather than a MIMO system. For these reasons the problem of input–output decoupling is of great practical importance. A block diagram representation of input–output decoupling via state feedback is given in Figure 10.6. 10.4.1 Decoupling via State Feedback For the case of input–output decoupling via state feedback the following theorem holds which was first proven by Falb and Wolovich [9]. Theorem 10.4.1 System (10.2-1) can be decoupling using the state-variable feedback law (10.2-2), if and only if the matrix Bþ , where 3 2 c1 A d 1 B 67 7 6 6 c A d2 B 7 7 6 2 7 6 ð10:4-6Þ Bþ ¼ 6 7 7 6 .. 7 6 . 7 6 45 cm Adm B is regular, i.e., jBþ j 6¼ 0, where ci is the ith row of matrix C and d1 ; d2 ; . . . ; dm are integers, which are defined as follows:
450
Chapter 10
Figure 10.6
Input–output decoupling via state feedback. (a) Open-loop system: x ¼ Ax þ Bu, y ¼ Cx; (b) closed-loop system: x ¼ ðA þ BFÞx þ BGr, y ¼ Cx; (c) closedloop system transfer function: HðsÞ ¼ CðsI A BFÞBG ¼ diagfh11 ðsÞ; h22 ðsÞ; . . . ; hmm ðsÞg.
di ¼
min j : ci Aj B 6¼ 0; j ¼ 0; 1; . . . ; n 1 for all j n1 if ci A j B ¼ 0
ð10:4-7
A pair of matrices F and G which satisfy the problem of decoupling is the following F ¼ ðBþ Þ1 Aþ
ð10:4-8aÞ
G ¼ ðBþ Þ1
ð10:4-8bÞ
where matrix Aþ and the transfer function matrix HðsÞ ¼ CðsI A BFÞ1 BG of the closed-loop system have the following forms:
State-Space Design Methods
2
c1 Ad1 þ1
3
7 6 7 6 7 6 6 d2 þ1 7 7 6 c2 A 7 6 7 6 þ A ¼6 7 7 6 .. 7 6 7 6 . 7 6 7 6 5 4 dm þ1 cm A
and
451
2 1 6 sd1 þ1 6 6 6 0 6 HðsÞ ¼ 6 6 . 6 . 6 . 4 0
0
1
sd1 þ1 .. . 0
..
.
3 0
7 7 7 0 7 7 7 .. 7 7 . 7 5 1
ð10:4-9Þ
sdm þ1
From Theorem 10.4.1 we conclude that in order to solve the input–output decoupling problem one must first construct the matrix Bþ and then calculate its determinant. If jBþ j ¼ 0 it follows that decoupling is not possible via feedback of the form (10.2-2), i.e., no matrices F and G exist such that the closed-loop transfer function matrix HðsÞ is diagonal and regular. In this case and provided that the open-loop system is invertible, i.e., det½CðsI AÞ1 B 6¼ 0, one seeks a ‘‘dynamic’’ form of state feedback (not considered in this book) to solve the problem. But if jBþ j 6¼ 0, decoupling is possible using Eq. (10.2-2), and a simple form of the matrices F and G that make HðsÞ regular and diagonal is given by relation (10.4-8). For the general form of F and G, involving arbitrary parameters, see [19]. Example 10.4.1 Consider the system of the form (10.2-1), where 1 1 4 1 2 ; C¼ ; B¼ A¼ 1 1 4 2 3
0 1
Find matrices F and G such that the closed-loop system is input–output decoupled. Solution Determine the integers d1 and d2 according to definition (10.4-7). We have 1 4 ¼ ½ 1 4 6¼ 0 c1 A0 B ¼ ½ 1 0 1 4 1 4 c2 A0 B ¼ ½ 1 1 ¼ ½0 0 ¼ 0 1 4 1 2 1 4 1 ¼ ½ 2 8 6¼ 0 c2 A B ¼ ½ 1 1 2 3 1 4 Therefore d1 ¼ 0 and d2 ¼ 1. Consequently, matrix Bþ has the form 2 3 2 3 3 2 c1 B 1 4 c1 A d 1 B Bþ ¼ 4 5 ¼ 4 5 ¼ 4 5 c2 AB 2 8 c2 A d 2 B Examining the determinant of the matrix Bþ shows that jBþ j ¼ 0. Therefore, we conclude that the system under control cannot be decoupled using the linear state feedback law (10.2-2).
452
Chapter 10
Example 10.4.2 Consider a system of the form (10.2-1), where 2 3 2 3 1 0 2 1 0 A ¼ 4 0 1 1 5; B ¼ 4 1 2 5; 1 2 0 1 2
1 C¼ 0
0 1
0 1
Find matrices F and G such that the closed-loop system is input–output decoupled. Solution Determine the integers d1 and 2 1 c1 A0 B ¼ ½ 1 0 0 4 1 1 2 1 c2 A0 B ¼ ½ 0 1 1 4 1 1 2 1 c2 A1 B ¼ ½ 0 1 1 4 0 1
d2 using definition (10.4-7). We have 3 0 2 5 ¼ ½ 1 0 6¼ 0 2 3 0 2 5 ¼ ½ 0 0 ¼ 0 2 32 3 0 2 1 0 1 1 54 1 2 5 ¼ ½ 1 4 6¼ 0 2 0 1 2
Therefore, d1 ¼ 0 and d2 ¼ 1. Consequently, the matrix Bþ has the form 2 3 2 3 2 3 1 0 c1 B c1 Ad1 B þ B ¼ 45 ¼ 45 ¼ 4 5 1 4 c2 AB c2 Ad2 B Examining the determinant of the matrix Bþ shows that jBþ j ¼ 4. Consequently, the system under control can be decoupled using the feedback law (10.2-2). To determine the matrices F and G according to relation (10.4-8), we must first compute the matrix Aþ . We have 2 3 2 3 2 3 c1 A 1 0 2 c1 Ad1 þ1 Aþ ¼ 4 5 ¼ 4 5 ¼ 4 5 2 5 1 c2 A2 c2 Ad2 þ1 Hence, the matrices G and F are given by 1 4 0 G ¼ ðBþ Þ1 ¼ and 4 1 1
F ¼ ðBþ Þ1 Aþ ¼
1 4 4 3
0 5
8 1
The decoupled closed-loop system is the following x ¼ ðA þ BFÞx þ BGr and y ¼ Cx If we substitute the matrices A, B, C, F, and G in the above state equations we obtain 2 3 2 3 0 0 0 2 0 1 1 1 0 0 x ¼ 4 1 3 1 5x þ 4 1 5 1 r and y¼ x 0 1 1 2 2 3 9 3 1 1 The transfer function matrix HðsÞ of the closed-loop system is given by
State-Space Design Methods
453
HðsÞ ¼ CðsI A BFÞ1 BG ¼
1=sd1 þ1 0
0 1=sd2 þ1
¼
1=s 0
0 1=s2
As expected, the matrix HðsÞ is diagonal and regular. Using the relation YðsÞ ¼ HðsÞRðsÞ we obtain 1 Y1 ðsÞ ¼ R1 ðsÞ s
and
Y2 ðsÞ ¼
1 R2 ðsÞ s2
Hence, it is clear that y1 ðsÞ is only a function of r1 ðsÞ and that y2 ðsÞ is only a function of r2 ðsÞ. The corresponding differential equations of the decoupled closed-loop system are dy1 ¼ r1 dt
and
d2 y 2 ¼ r2 dt2
Example 10.4.3 Consider the following system: x ðtÞ u ðtÞ x_ 1 ðtÞ ¼A 1 þB 1 ; x_ 2 ðtÞ x2 ðtÞ u2 ðtÞ
y1 ðtÞ x ðtÞ ¼C 1 y2 ðtÞ x2 ðtÞ
which it is assumed can be decoupled and for which c1 B 6¼ 0 and c2 B 6¼ 0, where c1 and c2 are the rows of the matrix C. (a) Find matrices F and G such that the closedloop system is decoupled and (b) determine the transfer function matrix of the decoupled closed-loop system. Solution (a) Since c1 B 6¼ 0 and c2 B 6¼ 0, it follows that d1 ¼ d2 ¼ 0. Thus, 2 3 2 3 c1 B c1 A and Aþ ¼ 4 5 ¼ CA Bþ ¼ 4 5 ¼ CB c2 B c2 A Consequently, the matrices F and G are given by the following relations: G ¼ ðBþ Þ1 ¼ ðCBÞ1
and
F ¼ ðBþ Þ1 Aþ ¼ ðCBÞ1 CA
(b) The transfer function of the closed-loop system is HðsÞ ¼ CðsI A BFÞ1 BG. If we expand HðsÞ in negative power series of s, we obtain " # I A þ BF ðA þ BFÞ2 HðsÞ ¼ C þ þ þ BG s s2 s3 Using the foregoing expressions for F and G, the matrix CðA þ BFÞ takes on the form CðA þ BFÞ ¼ C½A BðCBÞ1 CA ¼ CA CBðCBÞ1 CA ¼ 0 Consequently CðA þ BFÞk ¼ 0, for k 1 and therefore the transfer function of the closed-loop system reduces to
454
Chapter 10
HðsÞ ¼
CBG CBðCBÞ1 I ¼ ¼ ; s s s
where I the unit matrix
We can observe that the closed-loop system has been decoupled into two subsystems, each of which is a simple integrator. 10.4.2
Decoupling via Output Feedback
A simple approach to solve the problem of decoupling via output feedback is to use relation (10.2-6). This method requires prior knowledge of the matrices F and G. For any given pair of matrices F and G, which decouples the system under control, relation (10.2-6) may have a solution for K and N. Since N ¼ G, it follows that the problem of decoupling via output feedback has a solution provided that equation KC ¼ F can be solved for K. Note that the solution of the equation KC ¼ F is only a sufficient decoupling condition. This means that in cases where the equation KC ¼ F does not have a solution for K, it does not follow that the decoupling problem via output feedback does not have a solution. Results analogous to Subsec. 10.4.1 are difficult to derive for the case of output feedback and for this reason they are omitted here. For a complete treatment of this problem see [19].
10.5
EXACT MODEL MATCHING
The problem of exact model matching is defined as follows. Consider a system whose behavior is not satisfactory and a model whose behavior is the ideal one. Determine a control law such that the behavior of the closed-loop system follows exactly the behavior of the model. It is obvious that the solution of such a problem is of great practical importance, since it makes it possible to modify the behavior of a system so as to match an ideal one. In Sec. 9.1, the criterion (9.1-1) expresses the basic idea behind the problem of exact model matching, wherein we seek a controller such that the behavior of the closed-loop system follows, as closely as possible, the behavior of a model. Of course, this matching may become exact when the cost criterion (9.1-1) reduces to zero. The present section is devoted to this latter case—namely, to the exact model matching problem. The problem of exact model matching of linear time-invariant systems, from the algebraic point of view adopted in this chapter, is as follows. Consider a system under control described in state space by Eqs (10.2-1a and b) and a model described by its transfer function matrix Hm ðsÞ. Determine the controller matrices F and G of the feedback law (10.2-2) [or the matrices K and N of the feedback law (10.2-4)] so that the transfer function HðsÞ of the closed-loop system (10.2-3) (or the transfer ^ ðsÞ of the closed-loop system (10.2-5)) is equal to the transfer function matrix H function matrix of the model, i.e., such that ^ ðsÞÞ ¼ Hm ðsÞ HðsÞ ðor H where
ð10:5-1Þ
State-Space Design Methods
455
HðsÞ ¼ CðsI A BFÞ1 BG
ð10:5-2aÞ
^ ðsÞ ¼ CðsI A BKCÞ1 BN H
ð10:5-2bÞ
The solution of Eq. (10.5-1) for F and G is, in the general case, quite difficult. However, the solution of Eq. (10.5-1) for K and N is rather simple [15]. For this reason we will examine here this latter case. To this end, consider Figure 10.7. This figure is a closed-loop system with output feedback, where GðsÞ ¼ CðsI AÞ1 B is the transfer function matrix of the open-loop system under control (10.2-1). On the basis of this figure we obtain ^ ðsÞRðsÞ, where UðsÞ ¼ KYðsÞ þ NRðsÞ, YðsÞ ¼ GðsÞUðsÞ and, consequently, YðsÞ ¼ H ^ ðsÞ is the transfer function matrix of the closed-loop system of Figure 10.7 and it H is given by ^ ðsÞ ¼ ½Ip GðsÞK1 GðsÞN H
ð10:5-3Þ
where Ip is the p p identity matrix. Substituting Eq. (10.5-3), into Eq. (10.5-1) gives ½Ip GðsÞK1 GðsÞN ¼ Hm ðsÞ
ð10:5-4Þ
By premultiplying Eq. (10.5-4) by the matrix ½Ip GðsÞK, we obtain GðsÞN ¼ Hm ðsÞ GðsÞKHm ðsÞ or GðsÞN þ GðsÞKHm ðsÞ ¼ Hm ðsÞ
ð10:5-5Þ
Relation (10.5-5) is a polynomial equation in s, whose coefficients are matrices. Therefore, Eq. (10.5-5) has a solution only when a pair of matrices K and N exists such that the coefficients of all like powers of s in both sides of Eq. (10.5-5) are equal. If we carry out the matrix multiplications involved in Eq. (10.5-5) and appropriately group the results such that each side is a matrix polynomial in s and subsequently equate the coefficients of like powers in s, we obtain a linear algebraic system of equations having the general form Ph ¼ h
ð10:5-6Þ
where P and h are known matrices, while h is the unknown vector, whose elements are the elements of the matrices K and N. Relation (10.5-6) is, in general, a system of equations with more equations than unknowns. Solving Eq. (10.5-6) using the least-squares method we obtain h ¼ ðPT PÞ1 PT h
Figure 10.7
Closed-loop system with output feedback.
ð10:5-7Þ
456
Chapter 10
Expression (10.5-7) is the exact solution of system (10.5-6) when the error J ¼ eT e equals zero, where e ¼ PðPT PÞ1 Ph h. In this case, the solution h yields the matrices K and N which satisfy Eq. (10.5-5) (and, consequently, the relation of exact model matching (10.5-4)), exactly, provided that the matrix ½Ip GðsÞK is regular. This procedure is demonstrated by the example that follows. Example 10.5.1 Consider an unstable system under control with transfer function matrix GðsÞ and a stable model with transfer function matrix Hm ðsÞ, where 3 2 3 2 s 2 6s þ 17 s 2s 1 1 6 s2 þ s 1 7 7 6 ¼ ¼ ; Hm ðsÞ ¼ 4 GðsÞ ¼ 4 s þ 1 5 ðsÞ s þ 1 2 5 ðsÞ sðs þ 1Þ s2 þ s 1 s where ðsÞ ¼ s2 þ s 1 and ðsÞ ¼ sðs þ 1Þ. Determine the output controller matrices ^ ðsÞ of the closed-loop system is K and N such that the transfer function matrix H equal to the transfer function matrix Hm ðsÞ of the model. Solution In this example K ¼ ½k1 ; k2 and N ¼ n. Hence relation (10.5-5) becomes 2 3 2 3 2 3 2 3 s s 2s 2s 1 4 1 1 4 5½k1 ; k2 4 5 ¼ 45 5n þ ðsÞ ðsÞ ðsÞ ðsÞ sþ1 sþ1 2ðs þ 1Þ 2ðs þ 1Þ or
3 2 3 3 2 2sðsÞ ns ðsÞ 2s2 k1 þ 2sðs þ 1Þk2 4 5 þ 45 ¼ 45 2ðs þ 1ÞðsÞ nðs þ 1Þ ðsÞ 2ðs þ 1Þsk1 þ 2ðs þ 1Þ2 k2 2
The above relation can be written more compactly as follows: QðsÞh ¼ dðsÞ where
"
s ðsÞ 2s2 2sðs þ 1Þ QðsÞ ¼ ðs þ 1Þ ðsÞ 2sðs þ 1Þ 2ðs þ 1Þ2 2 3 n 2sðsÞ 6 7 dðsÞ ¼ h ¼ 4 k1 5; 2ðs þ 1ÞðsÞ k2
#
If we substitute the polynomials ðsÞ and ðsÞ in the matrix QðsÞ and in the vector dðsÞ we obtain s3 þ s2 2s2 2s2 þ 2s QðsÞ ¼ 3 ¼ Q0 þ Q1 s þ Qs2 s2 þ Q3 s3 s þ 2s2 þ s 2s2 þ 2s 2s2 þ 4s þ 2 where
State-Space Design Methods
Q0 ¼ Q3 ¼ and
dðsÞ ¼
where
d0 ¼
;
0 0
0
0 0 1 0
2 0
1 0
0
457
Q1 ¼
0
0 2
1
2 4
;
Q2 ¼
1
2 2
2
2 2
;
2s3 þ 2s2 2s ¼ d 0 þ d1 s þ d2 s 2 þ d3 s 3 2s3 þ 4s2 2
0 ; 2
d1 ¼
2 ; 0
d2 ¼
2 ; 4
d3 ¼
2 2
Consequently, relation QðsÞh ¼ dðsÞ can be rewritten as ½Q0 þ Q1 s þ Q2 s2 þ Q3 s3 h ¼ d0 þ d1 s þ d2 s2 þ d3 s3 For the above relation to hold, the vector h must be such that the coefficients of the like powers of s in both sides of the equation are equal, i.e., Q i h ¼ di ;
i ¼ 0; 1; 2; 3
This relation can be rewritten in the compact form of Eq. 10.5-6), i.e., in the form Ph ¼ h where
3 Q0 6 Q1 7 7 P¼6 4 Q2 5 ; Q3 2
3 d0 6 d1 7 7 h¼6 4 d2 5 d3 2
If we substitute the values of Qi and di in the equation Ph ¼ h and solve for h we obtain the exact solution h ¼ ½2; 1; 1T . Thus, the matrices N and K of the compensator are N ¼ 2 and K ¼ ½k1 ; k2 ¼ ½1; 1. To check the results, we substitute K ^ ðsÞ ¼ Hm ðsÞ. Hence, an exact model matching and N in Eq. (10.5-3), which yields H has been achieved. 10.6
STATE OBSERVERS
10.6.1 Introduction In designing a closed-loop system using modern control techniques, the control strategy applied is usually a feedback loop involving feedback of the system state vector xðtÞ. Examples of such strategies is the application of the state feedback law u ¼ Fx þ Gr for pole assignment, decoupling, and model matching presented in the previous sections. This means that for this type of feedback law to be applicable, the entire state vector x must be available (measurable). In practice, however, it happens very often that not all state variables of a system are accessible to measurement. This obstacle can be circumvented if a math-
458
Chapter 10
ematical model for the system is available, in which case it is possible to estimate the state vector. A widely known method for state-vector estimation or reconstruction is that of using an observer. This technique was first proposed by Luenberger [11–13] and is presented in the sequel. 10.6.2
State-Vector Reconstruction Using a Luenberger Observer
Consider the system ¼ AxðtÞ þ BuðtÞ xðtÞ yðtÞ ¼ CxðtÞ
ð10:6-1aÞ ð10:6-1bÞ
Assume that state vector xðtÞ is given approximately by the state vector x^ ðtÞ of the following system ^ ¼ A ^ x^ ðtÞ þ B^ uðtÞ þ KyðtÞ xðtÞ x
ð10:6-2Þ
^ , B^ , and K are unknown. where x^ is an n-dimensional vector and the matrices A System (10.6-2) is called the state observer of system (10.6-1). A closer examination of Eq. (10.6-2) shows that the observer is a dynamic system having two inputs, the input vector uðtÞ and the output vector yðtÞ of the initial system (10.6-1) (see Figure ^ , B^ , and K should be chosen such that x^ ðtÞ is as 10.8). Clearly, the observer matrices A close as possible to xðtÞ. In cases where x^ ðtÞ and xðtÞ are of equal dimension, then the observer is referred to as a full-order observer. This case is studied in the present section. When the dimension of x^ ðtÞ is smaller than that of xðtÞ, then the observer is referred to as a reduced-order observer, which is studied in Subsec. 10.6.3. Define the state error eðtÞ ¼ xðtÞ x^ ðtÞ
ð10:6-3Þ
The formal definition of the problem of designing the observer (10.6-2) is the follow^ , B^ , and K, such that the error eðtÞ tends to ing: determine appropriate matrices A zero as fast as possible. To solve the problem, we proceed as follows. Using Eqs (10.6-1) and (10.6-2), it can be shown that the error eðtÞ satisfies the differential equation ¼ xðtÞ xðtÞ ^ ½xðtÞ eðtÞ B^ uðtÞ KCxðtÞ eðtÞ x^ ¼ AxðtÞ þ BuðtÞ A or
Figure 10.8
Simplified presentation of the system (10.6-1) and the observer (10.6-2).
State-Space Design Methods
459
¼A ^ eðtÞ þ ½A KC A ^ xðtÞ þ ½B B ^ uðtÞ eðtÞ For the error eðtÞ to tend to zero, independently of xðtÞ and uðtÞ, the following three conditions must be satisfied simultaneously: 1. 2. 3.
^ ¼ A KC A B^ ¼ B ^ is stable. matrix A
From the above we conclude that the error eðtÞ satisfies the differential equation ¼A ^ eðtÞ ¼ ½A KCeðtÞ eðtÞ while the state observer (10.6-2) takes on the form ^ ¼ ½A KCx^ ðtÞ þ BuðtÞ þ KyðtÞ xðtÞ x
ð10:6-4aÞ
^ ¼ Ax^ ðtÞ þ BuðtÞ þ K½yðtÞ Cx^ ðtÞ xðtÞ x
ð10:6-4bÞ
or
According to Eq. (10.6-4a), the observer can be considered as a system involving the matrices A, B, and C of the original system together with an arbitrary matrix K. This ^ ¼ A KC effecmatrix K must be chosen so that the eigenvalues of the matrix A tively force the error eðtÞ to zero as fast as possible. According to Eq. (10.6-4b), the observer appears to be exactly the original system plus an additional term K½yðtÞ Cx^ ðtÞ. The term xðtÞ ¼ yðtÞ y^ ðtÞ ¼ yðtÞ Cx^ ðtÞ can be considered as a corrective term, often called a residual. Of course, if x^ ðtÞ ¼ xðtÞ, then xðtÞ ¼ 0. Therefore, a residual exists if the system output vector yðtÞ and the observer vector y^ ðtÞ ¼ Cx^ ðtÞ are different. Remark 10.6.1 To construct the observer it is necessary to construct the model of the original system itself, plus the corrective term K½yðtÞ Cx^ ðtÞ. One may then ask: Why not build the model xðtÞ x^ ¼ Ax^ ðtÞ þ BuðtÞ of the original system with initial condition x^ ðt0 Þ, and on the basis of this model estimate the state vector x^ ðtÞ? Such an approach is not used in practice because it presents certain serious drawbacks. The most important drawback is the following. Since x^ ðt0 Þ is only an estimate of xðt0 Þ, the initial condition xðt0 Þ of the system and the initial condition x^ ðt0 Þ of the model differ in most cases. As a result, x^ ðtÞ may not converge fast enough to xðtÞ. To secure rapid convergence of x^ ðtÞ to xðtÞ, we add the term K½yðtÞ C^ xðtÞ to the model xðtÞ x^ ¼ A^ xðtÞ þ BuðtÞ, resulting in an observer of the form (10.6-4b). Under the assumption that the system ðA; CÞ is observable, the matrix K provides adequate design flexibility, as shown below, so that x^ ðtÞ converges to xðtÞ very fast. The block diagram for the state observer (10.6-4) is presented in Figure 10.9. The state observer design problem therefore reduces to one of determining an ^ ¼ A KC lie in the appropriate matrix K, such that all eigenvalues of the matrix A left-half complex plane. A closer look at the problem reveals that it comes down to ^ ¼ A KC. As a matter of one of solving a pole-placement problem for the matrix A fact, this problem is dual to the pole-placement problem discussed earlier in Sec. 10.3. In what follows, we will use the results of Sec. 10.3 to solve the observer design problem.
460
Chapter 10
Figure 10.9
Block diagram of the observer (10.6-4).
As already mentioned in Sec. 10.3, the necessary and sufficient condition for a matrix F to exist, such that the matrix A BF may have any desired eigenvalues, is that the system ðA; BÞ is controllable, i.e., rankS ¼ n;
. . . where S ¼ ½B .. AB .. .. An1 B
ð10:6-5Þ
In the case of the observer, the necessary and sufficient condition for a matrix K to ^ ¼ A KC or, equivalently, the matrix A ^ T ¼ AT CT KT exist, so that the matrix A T T has any desired eigenvalues, is that the system ðA ; C Þ is controllable or, equivalently, that the system ðA; CÞ is observable, i.e., rankR ¼ n;
. . . where RT ¼ ½CT .. AT CT .. .. ðAT Þn1 CT
ð10:6-6Þ
Hence, the following theorem holds. Theorem 10.6.1 The necessary and sufficient conditions for the reconstruction of the state of system (10.6-1) is that the system is completely observable. For the system ðA; B; CÞ, we say that the conditions (10.6-5) and (10.6-6) are dual. We will first consider the single-output case for system (10.6-1). For this case, the matrix C reduces to a row vector cT , thus reducing R to the n n matrix: . . . RT ¼ ½c .. AT c .. .. ðAT Þn1 c ^ becomes A ^ ¼ A kcT , where kT ¼ ½k1 ; k2 ; . . . ; kn . Define The matrix A
State-Space Design Methods
461
pðsÞ ¼ jsI Aj ¼ sn þ a1 sn1 þ þ an ¼
n Y ðs i Þ i¼1
^ j ¼ sn þ a^1 sn1 þ þ a^ n ¼ p^ ðsÞ ¼ jsI A
n Y ðs ^i Þ i¼1
where i are the eigenvalues of the system (10.6-1) and ^i are the desired eigenvalues of the observer (10.6-4). Hence, the problem here is to find k so that the observer has the desired eigenvalues ^1 ; ^2 ; . . . ; ^n . The vector k sought is uniquely defined. In Sec. 10.3, three alternative methods were presented to solve for k. Applying the Buss– Gura formula (10.3-8) yields the following solution: k ¼ ½WT R1 ð^a aÞ where
2
1
a1
60 1 6 W¼6 6 .. .. 4. . 0 0 2 3 a1 6a 7 6 27 7 a¼6 6 .. 7 4 . 5 an
ð10:6-7Þ
an1
3
an2 7 7 .. 7 7; . 5
1
3 cT 7 6 T 6 c A 7 7; 6 R¼6 .. 7 4 5 . T n1 c A 2
3 a^ 1 6 a^ 7 6 27 7 a^ ¼ 6 6 .. 7; 4 . 5 2
and
a^ n
Clearly, the solution (10.6-7) corresponds to the solution (10.3-8). For the multi-output case, determining the matrix K, as discussed in Sec. 10.3, is usually a complicated task. A simple approach to the problem is to assume that K has the following outer product form: K ¼ qpT
ð10:6-8Þ
^ ¼ A KC ¼ where q and p are n-dimensional vectors. Then A T T T T A qp C ¼ A q , where c ¼ p C. Therefore, assuming K to be of the form (10.6-8), the multi-output case reduces to the single-output case studied previously. Hence, the solution for q is given by Eq.. (10.6-7), where the matrix R must be . . ~ , where R ~ T ¼ ½c .. AT c .. .. ðAT Þn1 c. It is noted that replaced by the matrix R the vector c ¼ CT p involves arbitrary parameters, which are the elements of the arbitrary vector p. These arbitrary parameters may take any values as long as ~ ¼ n. If the condition rankR ~ ¼ n cannot be satisfied, other methods for deterrankR mining K may be found in the literature. 10.6.3 Reduced-Order Observers Suppose that the matrix C in the output equation yðtÞ ¼ CxðtÞ is square and nonsingular. Then xðtÞ ¼ C1 yðtÞ, thus eliminating the need for an observer. Now, assume that only one of the state variables is not accessible to measurement. Then, it is reasonable to expect that the required state observer will not be of order n, but of lower order. This is in fact true, and can be stated as a theorem.
462
Chapter 10
Theorem 10.6.2 If system (10.6-1) is observable, then the smallest possible order of the state observer is n p. We will next present some useful results regarding the design of reduced-order observers. To this end, we assume that the vector xðtÞ and the matrices A and B may be decomposed as follows: A11 A12 B1 q ðtÞ xðtÞ ¼ 1 ; A¼ ; B¼ q2 ðtÞ A21 A22 B2 Thus q 1 ðtÞ ¼ A11 q1 ðtÞ þ A12 q2 ðtÞ þ B1 uðtÞ q ðtÞ ¼ A q ðtÞ þ A q ðtÞ þ B uðtÞ 2
21 1
22 2
ð10:6-9Þ
2
where q1 ðtÞ is a vector whose elements are all the measurable state variables of xðtÞ, i.e., yðtÞ ¼ C1 q1 ðtÞ;
with
jC1 j 6¼ 0
ð10:6-10Þ
In cases where the system is not in the form of Eqs (10.6-9) and (10.6-10), it can easily be converted to this form by using an appropriate transformation matrix. The observer of the form (10.6-4b) for the system (10.6-9) and (10.6-10) will then become q^ 1 ðtÞ ¼ A11 q^ 1 ðtÞ þ A12 q^ 2 ðtÞ þ B1 uðtÞ þ K1 ½yðtÞ C1 q^ 1 ðtÞ q^ ðtÞ ¼ A q^ ðtÞ þ A q^ ðtÞ þ B uðtÞ þ K ½yðtÞ C q^ ðtÞ 2
21 1
22 2
2
2
1 1
ð10:6-11aÞ ð10:6-11bÞ
According to Eq. (10.6-10), we have q1 ðtÞ ¼ q^ 1 ðtÞ ¼ C1 1 yðtÞ
ð10:6-12Þ
Therefore, there is no need for the observer (10.6-11a), while the observer (10.6-11b) becomes q^ 2 ðtÞ ¼ A22 q^ 2 ðtÞ þ B2 uðtÞ þ A21 C1 yðtÞ
ð10:6-13Þ
where use was made of Eq. (10.6-12). The observer (10.6-13) is a dynamic system of order equal to the number of the state variables which are not accessible to measurement. It is obvious that for the observer (10.6-13), the submatrix A22 plays an important role. If A22 has by luck satisfactory eigenvalues, then system (10.6-13) suffices for the estimation of q2 ðtÞ. On the other hand, if the eigenvalues of A22 are not satisfactory, then the following observer is proposed for estimating q2 ðtÞ: q^ 2 ðtÞ ¼ ryðtÞ þ vðtÞ
ð10:6-14Þ
where vðtÞ is an ðn pÞ vector governed by the vector difference equation ¼ FvðtÞ þ HuðtÞ þ GyðtÞ vðtÞ ð10:6-15Þ Define the error as before, i.e., let e ðtÞ 0 q ðtÞ q^ 1 ðtÞ ¼ 1 ¼ eðtÞ ¼ xðtÞ x^ ðtÞ ¼ 1 e2 ðtÞ e2 ðtÞ q2 ðtÞ q^ 2 ðtÞ The differential equation for e2 ðtÞ is the following:
State-Space Design Methods
463
vðtÞ e2 ðtÞ ¼ q 2 ðtÞ q^ 2 ðtÞ ¼ A21 q1 ðtÞ þ A22 q2 ðtÞ þ B2 uðtÞ r yðtÞ After some algebraic manipulations and simplifications, we have e2 ðtÞ ¼ Fe2 ðtÞ þ ðA21 rC1 A11 GC2 þ FrC1 Þq1 ðtÞ
ð10:6-16Þ
þ ðA22 rC1 A12 FÞq2 ðtÞ þ ðB2 rC1 B1 HÞuðtÞ
In order for e2 ðtÞ to be independent of q1 ðtÞ, q2 ðtÞ and uðtÞ, as well as to tend rapidly to zero, the following conditions must hold: 1.
GC2 ¼ A21 rC1 A11 þ FrC1
or
G ¼ ðA21 rC1 A11 ÞC1 1 þ Fr (10.6-17a)
2.
F ¼ A22 rC1 A12
(10.6-17b)
3.
H ¼ B2 rC1 B1
(10.6-17c)
4.
Matrix F is stable
If the foregoing conditions are met, then Eq. (10.6-16) becomes e ðtÞ ¼ Fe ðtÞ 2
2
The matrix r may be chosen such that the matrix F ¼ A22 rC1 A12 has any desired eigenvalues, as long as the system ðA22 ; C1 A12 Þ is observable, i.e., as long as rankR1 ¼ n p; T R1 ¼ ½C1 A12 T
where .. T . .. T np1 T . T . A22 ½C1 A12 . . ½A22 ½C1 A12
The following useful theorem has been proven [6]. Theorem 10.6.3 The pair ðA22 ; C1 A12 Þ is observable, if and only if the pair ðA; CÞ is observable. The final form of the observer (10.6-15) is ¼ FvðtÞ þ HuðtÞ þ ½ðA rC A ÞC1 þ FryðtÞ vðtÞ 21
1
11
1
or ¼ F^q ðtÞ þ HuðtÞ þ ðA rC A ÞC1 yðtÞ vðtÞ 21 1 11 1 2
ð10:6-18Þ
The block diagram of the observer (10.6-18) is presented in Figure 10.10. 10.6.4 Closed-Loop System Design Using State Observers Consider the system ¼ AxðtÞ þ BuðtÞ; xðtÞ
yðtÞ ¼ CxðtÞ
ð10:6-19Þ
^ ¼ Ax^ ðtÞ þ BuðtÞ þ K2 ½yðtÞ Cx^ ðtÞ xðtÞ x
ð10:6-20Þ
with the state observer
Apply the control law uðtÞ ¼ K1 x^ ðtÞ Then, system (10.6-19) becomes
ð10:6-21Þ
464
Chapter 10
Figure 10.10
Block diagram of the reduced-order observer (10.6-18).
¼ AxðtÞ BK x^ ðtÞ xðtÞ 1
ð10:6-22Þ
and the observer (10.6-20) takes on the form ^ ¼ Ax^ ðtÞ BK1 x^ ðtÞ þ K2 ½CxðtÞ Cx^ ðtÞ xðtÞ x
ð10:6-23Þ
If we use the definition eðtÞ ¼ xðtÞ x^ ðtÞ, then Eq. (10.6-22) becomes ¼ AxðtÞ BK ½xðtÞ eðtÞ xðtÞ 1
or ¼ ðA BK ÞxðtÞ þ BK eðtÞ xðtÞ 1 1 Subtracting Eq. (10.6-23) from Eq. (10.6-22), we have ¼ ðA K CÞeðtÞ eðtÞ 2
ð10:6-24Þ ð10:6-25Þ
The foregoing results are very interesting, because they illustrate the fact that the matrix K1 of the closed-loop system (10.6-24) and the matrix K2 of the error equation (10.6-25) can be designed independently of each other. Indeed, if system ðA; BÞ is controllable, then the matrix K1 of the state feedback law (10.6-21) can be
State-Space Design Methods
465
chosen so that the poles of the closed-loop system (10.6-24) have any desired arbitrary values. The same applies to the error equation (10.6-25), where, if the system ðA; CÞ is observable, the matrix K2 of the observer (10.6-20) can be chosen so as to force the error to go rapidly to zero. This property, where the two design problems (the observer and the closed-loop system) can be handled independently, is called the separation principle. This principle is clearly a very important design feature, since it reduces a rather difficult design task to two separate simpler design problems. Figure 10.11 presents the closed-loop system (10.6-24) and the error equation (10.6-25). Figure 10.12 gives the block diagram representation of the closed-loop system with state observer. Finally, the transfer function Gc ðsÞ of the compensator defined by the equation UðsÞ ¼ Gc ðsÞYðsÞ will be Gc ðsÞ ¼ K1 ½sI A þ BK1 þ K2 CÞ1 K2
ð10:6-26Þ
The results above cover the case of the full-order observer (order n). In the case of a reduced-order observer, e.g., of an observer of order n p, similar results can be derived relatively easily. Remark 10.6.2 Consider the pole placement and the observer design problems. The pole-placement problem is called the control problem and it is rather a simple control design tool for improving the closed-loop system performance. The observer design problem is called the estimation problem, since it produces a good estimate of xðtÞ in cases where xðtÞ is not measurable. The solution of the estimation problem reduces to that of solving a pole-placement problem. In cases where an estimate of xðtÞ is used in the control problem, one faces the problem of simultaneous solving the estimation and the control problem. At first sight this appears to be a formidable task. However, thanks to the separation theorem, the solution of the combined problem of estimation and control breaks down to separately solving the estimation and the control problem. Since the solution of these two problems is essentially the same, we conclude that the solution of the combined problem of estimation and control requires twice the solution of the pole placement problem. These results are usually referred to as algebraic techniques and cover the case of deterministic environment (i.e., deterministic systems and signals).
Figure 10.11 (10.6-25).
Representation of closed-loop system (10.6-24) and the error equation
466
Figure 10.12
Chapter 10
Block diagram of closed-loop system with state observer.
Remark 10.6.3 Going from the algebraic design techniques pointed out in Remark 10.6.2 to optimal design techniques (see Chap. 11), one realizes that there is a striking analogy between the two approaches: the optimal control problem reduces to that of solving a firstorder matrix differential equation, known as the Ricatti equation. When we are in a
State-Space Design Methods
467
stochastic environment, the problem of estimating xðtÞ, known as Kalman filtering, also reduces to that of solving a Ricatti equation. Now, consider the case where Kalman filtering is needed to estimate xðtÞ, which subsequently is to be applied for optimal control (this is the well-known linear quadratic gaussian or LQG problem). Then, thanks again to the separation theorem, the combined estimation and control problem, i.e., the LQG problem, breaks down to solving two separate Ricatti equations, i.e., solving twice a Ricatti equation. Remark 6.10.4 Clearly, Remark 10.6.2 summarizes the crux of the algebraic design approach results, whereas Remark 10.6.3 summarizes the crux of the optimal design approach results. It is most impressive that in both cases the separation theorem holds, a fact which greatly facilitates the solution of the combined estimation and control problem. As one would expect, the difficulty in solving estimation and control problems increases as one goes from algebraic to optimal techniques and as one goes from deterministic (Luenberger observer) to stochastic environment (Kalman filtering and LQG problem). 10.6.5 Observer Examples Example 10.6.1 Consider the system ¼ AxðtÞ þ BuðtÞ; xðtÞ where
2
6 1 A ¼ 4 11 0 6 0
3 0 1 5; 0
yðtÞ ¼ cT xðtÞ 2
1 B ¼ 40 1
3 0 1 5; 0
and
2 3 1 c ¼ 405 0
Design: (a) A full-order state observer, i.e., of order n ¼ 3 (b) A reduced-order state observer, i.e., of order n p ¼ 3 1 ¼ 2 (c) The closed-loop system for both cases Solution (a) Examine the system’s observability. We have 2 3 1 6 25 . . RT ¼ ½c .. AT c .. ðAT Þ2 c ¼ 4 0 1 65 0 0 1 Since rankR ¼ 3, there exists a full-order state observer having the form ^ ¼ ½A kcT x^ ðtÞ þ BuðtÞ þ kyðtÞ xðtÞ x The characteristic polynomial of the open-loop system is pðsÞ ¼ s3 6s2 þ 11s 6. Suppose that the desired observer characteristic polynomial is chosen as p^ðsÞ ¼ ðs þ 1Þðs þ 3Þðs þ 4Þ ¼ s3 þ 8s2 þ 19s þ 12. From Eq. (10.6-7), we have
468
Chapter 10
2
1
6
11
2
3
6 7 W ¼ 40 1 6 5; 0 0 1 3 2 3 2 6 a1 7 6 7 6 a ¼ 4 a2 5 ¼ 4 11 5 6 a3
3 2 3 8 a^ 1 7 6 7 ^a ¼ 6 ^ 4 a2 5 ¼ 4 19 5; 12 a^ 3
and
and hence 2
3 2 3 k1 14 T 1 k ¼ 4 k2 5 ¼ ½W R ð^a aÞ ¼ 4 7 5 k3 18 (b) The system of the present example is in the form (10.6-9), where 11 0 ; and A22 ¼ A12 ¼ ½1 0; A21 ¼ A11 ¼ 6; 6 0 0 1 B1 ¼ ½1 0; B2 ¼ ; and c1 ¼ 1 1 0
and where q1 ðtÞ ¼ x1 ðtÞ
x2 ðtÞ q2 ðtÞ ¼ x3 ðtÞ
and
1 0
Here, q1 ðtÞ ¼ x1 ðtÞ ¼ yðtÞ. The proposed observer for the estimation of the vector q2 ðtÞ is q2 ðtÞ ¼ uyðtÞ þ vðtÞ where vðtÞ is a two-dimensional vector described by the vector difference equation ¼ FvðtÞ þ HuðtÞ þ gyðtÞ vðtÞ and where F ¼ A22 uc1 A12 ¼
0 0
1 ’1 ½1 0 ’2
0 ¼
’1 ’2
1 0
g ¼ ðA21 uc1 A11 Þc1 1 þ Fu # " 11 ’1 ’1 1 ’1 11 6’1 ’21 þ ’2 ¼ 6þ ¼ 6 ’2 0 ’2 ’2 6 6’2 ’1 ’2 0 1 ’1 ’1 1 H ¼ B2 uc1 B1 ¼ ½1 0 ¼ 1 0 1 ’2 0 ’2 Since
. 1 rankRT1 ¼ rank ½c1 A12 T .. AT22 ½c1 A12 T ¼ rank 0
0 ¼2 1
we can find a vector u such that the matrix F has the desired eigenvalues. The characteristic polynomial of A22 is p2 ðsÞ ¼ s2 . Let p^2 ðsÞ ¼ ðs þ 1Þðs þ 2Þ ¼ s2 þ 3s þ 2 be the desired characteristic polynomial of matrix F. From Eq. (10.6-7), we have
State-Space Design Methods
W¼
1 0
0 ; 1
a^ ¼
469
3 ; 2
a¼
0 0
and therefore ’1 3 T 1 u¼ ¼ ½W R1 ð^a aÞ ¼ ’2 2 Introducing the value of u into g, F, and H yields 3 3 1 36 ; H¼ ; F¼ g¼ 1 2 0 12
1 0
(c) Let pc ðsÞ ¼ ðs þ 1Þðs þ 2Þðs þ 3Þ ¼ s3 þ 6s2 þ 11s þ 6 be the desired characteristic polynomial . of .the closed-loop system. The system is controllable because rankS ¼ rank ½B .. AB .. A2 B ¼ 3. Consequently, a feedback matrix K1 exists such that the closed-loop system poles are the roots of pc ðsÞ ¼ s3 þ 6s2 þ 11s þ 6. Using Eqs (10.3-15) and (10.3-16), the following matrix may be determined: 12 0 0 K1 ¼ 0 0 0 Checking, we have that jsI ðA BK1 Þj ¼ s3 þ 6s2 þ 11s þ 6 ¼ pc ðsÞ. Of course, in the case of a full-order observer, k2 ¼ k, where k was determined in part (a) above. In the case of a reduced-order observer, k2 ¼ u, where u was determined in part (b) above. Example 10.6.2 Consider the system ¼ AxðtÞ þ buðtÞ; xðtÞ where A¼
0 1 ; 0
yðtÞ ¼ cT xðtÞ
b¼
0 ;
c¼
1 0
In this example we suppose that only the state x1 ðtÞ ¼ yðtÞ can be directly measured. Design: (a) A full-order state observer, i.e., of order n ¼ 2 (b) A reduced-order state observer, i.e., of order n p ¼ 2 1 ¼ 1. In other words, find an observer to estimate only the state x2 ðtÞ, which we assume it not accessible to measurement. Note that x1 ðtÞ is measurable since x1 ðtÞ ¼ yðtÞ. (c) The closed-loop system for both cases. Solution (a) Examine the system’s observability. We have . 1 0 RT ¼ ½c .. AT c ¼ 0 1 Since rankR ¼ n ¼ 2, there exists a full-order state observer having the form
470
Chapter 10
^ ¼ ½A kcT x^ ðtÞ þ BuðtÞ þ kyðtÞ xðtÞ x The characteristic polynomials pðsÞ and p^ðsÞ of the open-loop system and of the observer are pðsÞ ¼ jsI Aj ¼ s2 þ s and p^ðsÞ ¼ jsI ðA kcT Þj ¼ s2 þ a^1 s þ a^ 2 , respectively. From Eq. (10.6-7) we have 1 a^ W¼ ; a^ ¼ 1 ; and a¼ 0 0 1 a^ 2 and thus
k¼
k1 k2
¼ ½WT R1 ð^a aÞ ¼
a^ 1 a^2 ða^ 1 Þ
From a practical point of view, we choose a^ 1 and a^ 2 in p^ ðsÞ so that the error eðtÞ ¼ xðtÞ x^ ðtÞ tends rapidly to zero. Of course, both roots of p^ ðsÞ must lie in the left-hand complex plane. (b) The system of the present example is in the form (10.6-9), where A11 ¼ 0, A12 ¼ 1, A21 ¼ 0, A22 ¼ , b1 ¼ 0, b2 ¼ , and c1 ¼ 1. Here q1 ðtÞ ¼ x1 ðtÞ and q2 ðtÞ ¼ x2 ðtÞ. Moreover q^ 1 ðtÞ ¼ x1 ðtÞ ¼ yðtÞ. For the estimation of x2 ðtÞ the proposed observer is q2 ðtÞ ¼ x^ 2 ðtÞ ¼ ’yðtÞ þ vðtÞ where vðtÞ is a scalar function governed by the differential equation v_ ðtÞ ¼ f vðtÞ þ huðtÞ þ gyðtÞ and where f ¼ A22 ’c1 A12 ¼ ’ 2 g ¼ ðA21 ’c1 A11 Þc1 1 þ f ’ ¼ ð ’Þ’ ¼ ’ ’
h ¼ B2 ’c1 B1 ¼ Since rank RT1 ¼ rank ½ðc1 A12 ÞT ¼ rank ð1Þ ¼ 1 we can find a ’ such that f has the desired eigenvalue. Let be the desired eigenvalue of f . Then, ’ ¼ . Introducing the value of ’ into g and f we have g ¼ 2 þ
and
f ¼
(c) Let pc ðsÞ ¼ s þ 1 s þ 2 be the desired characteristic polynomial of the closed-loop ystem. The parameters 1 and 2 are arbitrary, but they will ultimately be specified in order to meet closed-loop system requirements. The system is con. trollable since rank S ¼ rank ½b .. Ab ¼ 2. Therefore, we can choose a feedback vector k1 such that the closed-loop system poles are the roots of pc ðsÞ ¼ s2 þ 1 s þ 2 . Using the results of Sec. 10.3, the following vector is determined: 1 kT1 ¼ 2 2
Checking the results, we have jsI ðA bkT1 Þj ¼ s2 þ 1 s þ 2 ¼ pc ðsÞ. Of course, in the case of a full-order observer, k1 ¼ k, where k has been determined in part (a) above. In the case of a reduced-order observer, k2 ¼ ’, where ’ has been determined in part (b) above. The block diagram representations of the closed-loop systems in both cases are given in Figures 10.13 and 10.14.
State-Space Design Methods
Figure 10.13 state observer.
471
Block diagram of the closed-loop system of Example 10.6.2 with a full-order
472
Figure 10.14
Chapter 10
Block diagram of the closed-loop system of Example 10.6.2 with a reducedorder state observer.
State-Space Design Methods
10.7
473
PROBLEMS
1. Consider the linear 2 1 1 A¼4 0 1 1 2
system with 2 3 3 0 1 1 5; b ¼ 4 0 5; 1 1
2 3 0 c ¼ 405 1
Find a state feedback control law of the form uðtÞ ¼ f T xðtÞ and an output feedback control law of the form uðtÞ ¼ kT yðtÞ, such that the closed-loop eigenvalues are 1, 2, and 3. 2. Consider the linear system with 3 2 3 2 1 1 4 1 3 4 1 4 7 6 7 6 ; C¼ B ¼ 4 1 0 5; A¼4 3 1 1 5; 0 0 0 1 1 5 1 3 0 0 D¼ 0 1 Find a state feedback control law of the form uðtÞ ¼ FxðtÞ and an output feedback control law of the form uðtÞ ¼ KyðtÞ, such that the closed-loop eigenvalues are 0, 2, and 2. 3. The lateral motion of a helicopter can be approximately described by the following third-order linear state-space model [2]: 2 3 2 32 3 2 3 q_ðtÞ 0:4 0 0:01 qðtÞ 6:3 4 _ðtÞ 5 ¼ 4 1 54 ðtÞ 5 þ 4 0 5ðtÞ 0 0 v_ðtÞ 1:4 9:8 0:02 vðtÞ 0:8 or ¼ AxðtÞ þ BuðtÞ xðtÞ where qðtÞ is the pitch rate, ðtÞ is the pitch angle of the fuselage, vðtÞ the horizontal velocity of the helicopter, and ðtÞ is the rotor inclination angle. Determine a state feedback control law of the form u ¼ f T x so that the closed-loop system eigenvalues are 2, 1 þ j, 1 j. 4. Consider the system of the inverted pendulum on a cart (Chap. 3, Sec. 3.14, Problem 10), where M ¼ 3 kg, m ¼ 200 g, I ¼ 60 cm. Find a state feedback control law u ¼ f T x, such that the eigenvalues of the closed-loop system are 2 þ j, 2 j, and 5. (b) Find an output feedback control law of the form u ¼ kT y which assigns the same eigenvalues.
(a)
5. Decouple via both state and output feedback the linear systems: 1 0 1 0 1 1 ðaÞ A ¼ ; B¼ ; C¼ 0 2 0 1 1 1 2 3 2 3 2 3 1 0 0 1 0 0 0 6 7 6 7 6 7 ðbÞ A ¼ 4 2 3 0 5; B ¼ 4 1 0 5; CT ¼ 4 1 0 5 0 1 1 1 1 0 1
474
Chapter 10
2
0
60 6 ðcÞ A ¼ 6 40 0
1 0 0 1 0 0 0 0
0
2
3
07 7 7; 05
2
07 7 7; 05
60 07 7 6 CT ¼ 6 7 40 05 0 1
0
0
1
60 6 B¼6 41
1
3
0
1 0
3
6. Consider controlling the lateral motion of the helicopter of Problem 3. If the output is the horizontal velocity v of the helicopter, determine the gains k and n of the output feedback control law of the form ¼ kv þ nref where ref is a reference input, such that the transfer function of the closed-loop system has the form HðsÞ ¼
9 s þ 3s2 þ 9s 2
7. Consider the linear system with 1 0 1 A¼ ; B¼ 0 2 0
0 ; 1
C¼
1 1
1 ; 0
D¼
1 0
1 0
Show that if the state feedback law (10.2-2) is applied to this system with 1 2 0 F¼ and G¼ 0 2 1 then the transfer function matrix of the closed-loop system becomes 2 3 ðs þ 1Þðs þ 2Þ 1 4 5 HðsÞ ¼ ðs þ 2Þðs þ 4Þ 2 Now assume that Hm ðsÞ ¼ HðsÞ and F and G are unknown matrices. Solve the exact model matching problem. 8. Show that if we apply the law (10.2-2) to the system of Problem 2 with 13 4 11 1 0 F¼ ; G¼ 12 3 12 1 1 then the transfer function matrix of the closed-loop system becomes 2 1 3 0 6 7 HðsÞ ¼ 4 s þ 3 5 s 1 sþ3 Now assume that Hm ðsÞ ¼ HðsÞ and F and G are unknown matrices. Solve the exact model matching problem. 9. Solve the exact model matching problem via output feedback for the following two cases:
State-Space Design Methods
a:
475
System under control 3 2 s 6 ðs þ 1Þðs þ 2Þ 7 7 GðsÞ ¼ 6 4 5 1
and
sþ1 2
b:
Model 3 1 6 ðs þ 2Þ 7 7 6 Hm ðsÞ ¼ 6 7 4 1 5 2
3 1 1 6 7 GðsÞ ¼ 4 sðs þ 1Þ 5 2=ðs þ 2Þ 1=s
and
sþ1 2 1 6s þ 1 Hm ðsÞ ¼ 6 4 1
3 1 s þ27 7 s 5 sþ1
10. Consider the helicopter of Problem 3. Let the pitch rate qðtÞ be the output of the system. Find (a) A full-order state observer (b) A reduced-order observer 11. The state-space model of a satellite position control system is as follows: ¼ AxðtÞ þ buðtÞ xðtÞ y ¼ cT xðtÞ where xT ¼ ½x1 ; x2 ¼ ½y ; ! with y the angular position and ! the angular velocity, and 1 0 0 1 ; c¼ ; b¼ A¼ 0 0 0 (a) Find a full-order state observer. (b) Find a reduced-order observer. (c) Draw the closed-loop system diagram for (a) and (b). 12. Consider the speed control system described in Subsec. 3.13.3. A description of the system in state space is as follows ¼ AxðtÞ þ buðtÞ xðtÞ y ¼ cT xðtÞ where xT ¼ ½x1 ; x2 , where x1 is the angular speed !m of the motor, x2 is the current if , u is the input voltage vf , and y is the angular speed !y of the load. The system matrices are 2 0 N A¼ 1 ; b¼ ; c ¼ 0 3 L1 0 f where
! B m Km2 1 ¼ þ ; Jm Jm Ra
2 ¼
Km Kg ; Jm Ra
and
3 ¼
Assume that only the state variable x1 ðtÞ can be measured. Then
Rf Kf
476
Chapter 10
(a) Find a full-order state observer. (b) Find a reduced-order state observer for the estimation of the current if , which is not measurable. (c) Draw the block diagram of the closed-loop system for both cases (a) and (b). 13. Consider the system 2 3 2 3 0 1 0 0 ¼ 4 0 0 1 5xðtÞ þ 4 0 5uðtÞ xðtÞ 1 3 3 1 yðtÞ ¼ ½ 1
1 0 xðtÞ
Determine the state feedback control law of the form uðtÞ ¼ f T xðtÞ, so that the closed loop eigenvalues are 1, 1:5, 2. (b) Find a full-order observer with characteristic polynomial p^ ðsÞ ¼ s3 þ 12s2 þ47s þ 60: (c) Draw the block diagram of the system incorporating the controller and the observer found in (a) and (b). (a)
BIBLIOGRAPHY Books 1. 2. 3. 4.
PJ Antsaklis, AN Michel. Linear Systems. New York: McGraw-Hill, 1997. GJ Franklin, JD Powell, A Emani-Naeini. Feedback Control of Dynamic Systems. 2nd ed. London: Addison-Wesley, 1991. T Kailath. Linear Systems. Englewood Cliffs, New Jersey: Prentice-Hall, 1980. J O’Reilly. Observers for Linear Systems. New York: Academic Press, 1983.
Articles 5. 6. 7. 8. 9. 10. 11. 12. 13.
J Ackermann. Parameter space design of robust control systems. IEEE Trans Automatic Control AC-25:1058–1072, 1980. AT Alexandridis, PN Paraskevopoulos. A new approach for eigenstructure assignment by output feedback. IEEE Trans Automatic Control AC-41:1046–1050, 1996. RW Brockett. Poles, zeros and feedback: state space interpretation. Trans Automatic Control AC-10:129–135, 1965. JC Doyle, G Stein. Robustness with observers. IEEE Trans Automatic Control AC24:607–611, 1979. PL Falb, WA Wolovich. Decoupling in the design and synthesis of multivariable control systems. IEEE Trans Automatic Control AC-12:651–659, 1967. EG Gilbert. Controllability and observability in multivariable control systems. J Soc Ind Appl Math Control Ser A, 1(2):128–151, 1963. DG Luenberger. Observing the state of a linear system. IEEE Trans Military Electronics MIL-8:74–80, 1964. DG Luenberger. Observers for multivariable systems. IEEE Trans Automatic Control AC-11:190–197, 1966. DG Luenberg. An introduction to observers. IEEE Trans Automatic Control AC16:596–602, 1971.
State-Space Design Methods 14. 15. 16. 17.
18.
19. 20. 21. 22. 23.
24. 25.
477
BC Moore. Principal component analysis in linear systems: controllability, observability and model reduction. IEEE Trans Automatic Control AC-26:17–32, 1981. PN Parskevopoulos. Exact transfer-function design using output feedback. Proc IEE 123:831–834, 1976. PN Parskevopoulos. A general solution to the output feedback eigenvalue assignment problem. Int J Control 24:509–528, 1976. PN Parskevopoulos, FN Koumboulis. Decoupling and pole assignment in generalized state space systems. IEE Proc, Part D, Control Theory and Applications 138:547–560, 1991. PN Paraskevopoulos, FN Koumboulis. A unifying approach to observers for regular and singular systems. IEE Proc, Part D, Control Theory and Applications 138:561–572, 1991. PN Paraskevopoulos, FN Koumboulis. A new approach to the decoupling problem of linear time-invariant systems. J Franklin Institute 329:347–369, 1992. PN Paraskevopoulos, FN Koumboulis. The decoupling of generalized state space systems via state feedback. IEEE Trans Automatic Control AC-37:148–152, 1992. PN Paraskevopoulos, FN Koumboulis. Observers for singular systems. IEEE Trans Automatic Control AC-37:1211–1215, 1992. PN Paraskevopoulos, SG Tzafestas. New results on feedback modal-controller design. Int J Control 24:209–216, 1976. PN Paraskevopoulos, FN Koumboulis, DF Anastasakis. Exact model matching of generalized state space systems. J Optimization Theory and Applications (JOTA) 76:57–85, 1993. JC Willems, SK Mitter. Controllability, observability, pole allocation, and state reconstruction. IEEE Trans Automatic Control AC-16:582–595, 1971. WM Wonham. On pole assignment in multi-input controllable linear systems. IEEE Trans Automatic Control AC-12:660–665, 1967.
11 Optimal Control
11.1
GENERAL REMARKS ABOUT OPTIMAL CONTROL
Optimal control deals with the solution of one of the most celebrated problems of modern control theory. Generally speaking, this problem is defined as the determination of the best possible control strategy (usually of the optimum control vector uðtÞ), which minimizes a certain cost function or performance index. In this chapter we consider the optimal control of linear systems (the nonlinear case is far too difficult for the level of this book). The system under control is described in state space by the equations ¼ AðtÞxðtÞ þ BðtÞuðtÞ xðtÞ ð11:1-1aÞ yðtÞ ¼ CðtÞxðtÞ þ DðtÞuðtÞ
ð11:1-1bÞ
xðt0 Þ ¼ x0
ð11:1-1cÞ
where the matrices AðtÞ, BðtÞ, CðtÞ, and DðtÞ have dimensions n n, n m, p n, and p m, respectively. The objective of the optimal control problem is to determine a control vector uðtÞ which will ‘‘force’’ the behavior of the system under control to minimize some type of cost function, while at the same time satisfying the physical constraints of the system—namely, the state equations (11.1-1). The cost criterion is usually formulated so as to express some physical quantity. This way, the very idea of minimization of a cost criterion has a practical meaning, as for example the minimization of the control effort energy, the energy dissipated by the system, etc. A particular form of the cost function, which in itself is very general, is the following: ð tf t ¼ tf J ¼ ½xðtÞ; t þ ’½xðtÞ; uðtÞ; t dt ð11:1-2Þ t ¼ t0 t0 The first term in Eq. (11.1-2) refers to the cost on the boundaries of the optimization time interval ½t0 ; tf . More precisely, ½xðt0 Þ; t0 is the cost at the beginning, while ½xðtf Þ; tf is the cost at the end of the interval. The second term in Eq. (11.1-2) is an integral which refers to the cost in the entire optimization interval. 479
480
Chapter 11
Depending on the requirements of the particular optimization problem, the functions ½xðtÞ; t and ’½xðtÞ; uðtÞ; t take on special forms. In the sequel, some of the most well-known special forms of Eq. (11.1-2) are given, together with a description of the corresponding optimal control problem. 1 The Minimum Time Control Problem In this case the cost function has the form ð tf J¼ dt ¼ tf t0
ð11:1-3Þ
t0
It is obvious that this criterion refers only and exclusively to the time duration tf t0 . The control problem here is to find a control vector uðtÞ such that the time required for xðtÞ to go from its initial state xðt0 Þ to its final state xðtf Þ, is a minimum. Examples of ‘‘final states’’ are the crossing of the finish line by a car or a sprinter in a race, the time needed to complete a certain task, etc. 2 The Terminal Control Problem In this case the cost function has the form J ¼ ½xðtf Þ mðtf ÞT S½xðtf Þ mðtf Þ
ð11:1-4Þ
where mðtf Þ is the desired final value of the vector xðtÞ and S is an n n real, symmetric, positive semidefinite weighting matrix (see Sec. 2.12). This form of J shows clearly that here our attention has been exclusively concentrated on the final value xðtf Þ of xðtÞ. Here, we want to determine a control vector uðtÞ so that the error xðtf Þ mðtf Þ is minimal. An example of such a problem is the best possible aiming at a point on earth, in the air, on the moon, or elsewhere. 3 The Minimum Control Effort Problem In this case the cost function has the form ð tf J¼ uT ðtÞRðtÞuðtÞ dt
ð11:1-5Þ
t0
where RðtÞ is an m m real, symmetric, positive definite weighting matrix for t 2 ðt0 ; tf Þ. This expression for J represents the energy consumed by the control vector uðtÞ in controlling the system. We want this energy to be the least possible. As an example, consider the gasoline and break pedals of a car. The driver must use each pedal in such a way as to reach his destination by consuming the least possible fuel. Note that the present cost criterion is essentially the same with the cost criterion Ju of relation (9.10-2) which is used in the classical optimal control techniques (Sec. 9.10). 4 The Optimal Servomechanism or Tracking Problem In this case the cost function has the form ð tf ð tf T J ¼ ½xðtÞ mðtÞ QðtÞ½xðtÞ mðtÞ dt ¼ eT ðtÞQðtÞeðtÞ dt t0
t0
ð11:1-6Þ
Optimal Control
481
where QðtÞ is an n n real, symmetric, positive semidefinite weighting matrix for t 2 ðt0 ; tf Þ and mðtÞ is the prespecified desired path of the state vector xðtÞ. The vector eðtÞ ¼ xðtÞ mðtÞ is the error, which we want to minimize. This may be accomplished by determining an appropriate uðtÞ so that J in Eq. (11.-6) becomes minimal. The track of a space shuttle, the desired course of a missile, of a ship, of a car or even of a pedestrian, are optimal control problems of the form (11.1-6). If we desire to incorporate the least-effort problem (11.1-5), then Eq. (11.1-6) takes on the more general form J¼
ð tf
½xðtÞ mðtÞT QðtÞ½xðtÞ mðtÞ þ uT ðtÞRðtÞuðtÞ dt
ð11:1-7Þ
t0
We often also want to include the terminal control problem, in which case Eq. (11.1-7) takes on the even more general form J ¼ ½xðtf Þ mðtf ÞT S½xðtf Þ mðtf Þ ð tf
þ ½xðtÞ mðtÞT QðtÞ½xðtÞ mðtÞ þ uT ðtÞRðtÞuðtÞ dt
ð11:1-8Þ
t0
5 The Optimal Regulator Problem In this case the cost function is a special case of Eq. (11.1-8), where mðtÞ ¼ 0. Hence, in this case we have J ¼ xT ðtf ÞSxðtf Þ þ
ð tf
xðtÞT QðtÞxðtÞ þ uT ðtÞRðtÞuðtÞ dt
ð11:1-9Þ
t0
A well-known optimal regulator example is the restoring of a system to its equilibrium position after it has been disturbed. It is noted that the weighting matrices S, QðtÞ, and RðtÞ are chosen according to the ‘‘weight,’’ i.e., to the importance we want to assign to each element of the error vector eðtÞ ¼ xðtÞ mðtÞ and the input vector uðtÞ. The choice of suitable S, QðtÞ, and RðtÞ for a specific problem is usually difficult and requires experience and engineering insight. Among the many problems that have been solved thus far using the modern optimal control techniques, only two are presented in this book due to space limitations. These problems are the optimal linear regulator (Sec. 11.3) and the optimal linear servomechanism (Sec. 11.4). These two problems are of great theoretical as well as of practical interest. For more information on these problems, as well as on other problems of optimal control (for example bang-bang control, stochastic control, adaptive control, etc.) see [1–20]. To facilitate the study of the optimal linear regulator and servomechanism control problems, we present the necessary mathematical background in Sec. 11.2 that follows. This mathematical background covers two very basic topics: (i) maxima and minima using the calculus of variations and (ii) the maximum principle.
482
Chapter 11
11.2
MATHEMATICAL BACKGROUND
11.2.1
Maxima and Minima Using the Method of Calculus of Variations
In what follows, we will use the method of calculus of variations to study the following two problems: maxima and minima of a functional without constraints and maxima and minima of a functional with constraints. 1 Maxima and Minima of a Functional Without Constraints Consider the cost function or performance index ð tf JðxÞ ¼ ’½xðtÞ; x_ ðtÞ; t dt
ð11:2-1Þ
t0
This performance index JðxÞ is a functional, i.e., JðxÞ is a function of another function, namely, of the function xðtÞ. We are asked to find a function xðtÞ in the interval ½t0 ; tf such that JðxÞ is a minimum. A convenient method to solve this problem is to apply the method of calculus of variations presented in the sequel. Let xðtÞ and x_ ðtÞ be presented as follows: xðtÞ ¼ x^ ðtÞ þ "ðtÞ ¼ x^ ðtÞ þ x
ð11:2-2aÞ
x_ ðtÞ ¼ x^_ ðtÞ þ "_ ðtÞ ¼ x^_ ðtÞ þ x_
ð11:2-2bÞ
where x^ ðtÞ is an admissable optimal trajectory, i.e., x^ ðtÞ minimizes J, ðtÞ is a deviation of xðtÞ and " is small number. Substitute Eq. (11.2-2) in Eq. (11.2-1). Next, expand ’ðx; x_ ; tÞ in Taylor series about the point " ¼ 0, to yield
@’ @’ ’ x^ ðtÞ þ "ðtÞ; x^_ ðtÞ þ "_ ðtÞ; t ¼ ’ x^ ; x^_ ; t þ "ðtÞ þ "_ ðtÞ þ hot ð11:2-3Þ @x^ @x^_ where hot stands for higher-order terms and includes all the Taylor series terms which involve " raised to a power equal or greater than two. Let J be a small deviation of J from its optimal value, i.e., let J ¼ J½x^ þ "ðtÞ; x^_ þ "_ ðtÞ; t Jðx^ ; x^_ ; tÞ
ð11:2-4Þ
Substitute Eq. (11.2-3) in Eq. (11.2-4) and, using Eq. (11.2-1), we have ð tf h i ’½x^ þ "ðtÞ; x^_ þ "_ ðtÞ; t ’ðx^ ; x^_ ; tÞ dt J ¼ t0
ð tf @’ @’ "ðtÞ þ "_ ðtÞ þ hot dt ¼ ^ @x^_ t0 @ x ð tf @’ @’ x þ x_ þ hot dt ¼ ^ @x^_ t0 @ x
ð11:2-5Þ
where x ¼ "ðtÞ and x_ ¼ "_ ðtÞ. Let J be the linear part of J with respect to x and x_ . Then J takes on the form ð tf @’ @’ x þ x_ dt ð11:2-6Þ J ¼ ^ @x^_ t0 @ x The following theorem holds.
Optimal Control
483
Theorem 11.2.1 A necessary condition for J to be maximum or minimum when xðtÞ ¼ x^ ðtÞ is that J ¼ 0. If we apply Theorem 11.2.1 to Eq. (11.2-6) and if, for simplicity, we drop the symbol ‘‘ ^ ’’ from the optimal trajectory x^ ðtÞ, then we readily have ð tf @’ @’ x þ x_ dt ¼ 0 ð11:2-7Þ @x_ t0 @x The above integral may be simplified by using the ‘‘integration by parts’’ method, as follows. Let b ð b ðb udv ¼ uv vdu a
a
a
where u¼ Then
@’ @x_
and
dv ¼ x_ dt ¼ dðxÞ
@’ d @’ du ¼ d ¼ dt @x_ dt @x_
and
v ¼ x
Hence, the second term in Eq. (11.2-7) becomes t ð tf ð tf ð tf @’ @’ @’ f d @’ x_ dt ¼ dðxÞ ¼ x ðxÞ dt _ _ @x_ dt @x_ t 0 @x t0 @ x t0 t0 Thus the integral (11.2-7) may be written as t ð tf @’ d @’ @’ f xdt þ x dt @x_ @x_ t0 @x t0
ð11:2-8Þ
For Eq. (11.2-8) to be equal to zero, independently of the variation x, the following two conditions must hold simultaneously: @’ d @’ ¼0 ð11:2-9Þ @x dt @x_ @’ x ¼ 0; @x_
for t ¼ t0 and tf
ð11:2-10Þ
Equation (11.2-9) is the Euler–Lagrange equation and Eq. (11.2-10) represents the boundary conditions of the problem. The linear portion 2 L of the second differential 2 L may be determined in a similar way to that of determining J, to yield # " # ð "" 1 tf @2 ’ d @2 ’ @2 ’ 2 2 2 J¼ ð11:2-11Þ ðxÞ þ 2 ðx_ Þ dt 2 t0 @x2 dt @x@x_ @x_ For J to be maximum (minimum), there must be 2 J 0 (2 J 0). For example, for J to be minimum, from Eq. (11.20-11) it follows that the following relations must hold:
484
Chapter 11
" # @2 ’ d @2 ’ 0 @x2 dt @x@x_
@2 ’ 0 @x_ 2
and
ð11:2-12Þ
Remark 11.2.1 With regard to the boundary conditions (11.2-10), we distinguish the following four cases: Case 1. The trajectory xðtÞ is fixed at t0 and tf , in which case xðt0 Þ ¼ C1
xðtf Þ ¼ C2
and
where C1 and C2 are the given constants. In this case, no restriction is placed upon @’=@x_ . Case 2. The trajectory xðtÞ is fixed at t0 and free at tf , in which case xðt0 Þ ¼ C1
@’ ¼ 0; @x_
and
for
t ¼ tf
The condition @’=@x_ ¼ 0 for t ¼ tf is because since we don’t know the value of xðtÞ at tf , it follows that we don’t know x for t ¼ tf . Hence, to satisfy the boundary condition (11.2-10) at t ¼ tf we must have @’=@x_ ¼ 0 for t ¼ tf . Case 3. The trajectory xðtÞ is free at t0 and fixed at tf , in which case @’ ¼0 @x_
for
t ¼ t0
and
xðtf Þ ¼ C2
Case 4. The trajectory xðtÞ is free at both t0 and tf , in which case @’ ¼0 @x_
for
t ¼ t0
and
t ¼ tf
Remark 11.2.2 The results of this section can readily be expanded to cover the more general case where xðtÞ is no longer a scalar function but rather a vector function, i.e., when ð tf tÞ dt; J¼ ’ðx; x; where xT ¼ ðx1 x2 ; . . . ; xn Þ ð11:2-13Þ t0
Here, the Euler–Lagrange equation is @’ d @’ ¼0 @x dt @ x
ð11:2-14Þ
and the boundary conditions are @’ ðxÞT ¼ 0; @x
for
t ¼ t0
and
tf
ð11:2-15Þ
Optimal Control
485
Remark 11.2.3 From the foregoing material it follows that the problem of determining the maxima and minima of a functional using the calculus of variations reduces to that of solving a two-point boundary value problem (TPVBP). Example 11.2.1 Determine the optimum xðtÞ which minimizes the cost function ð tf ð =2 J¼ ’½xðtÞ; x_ ðtÞ; t dt ¼ ½x2 ðtÞ x_ 2 ðtÞ dt 0
t0
with boundary conditions xðt0 Þ ¼ xð0Þ ¼ 0 and xðtf Þ ¼ xð=2Þ ¼ 1. Solution The Euler–Lagrange equation is @’ d @’ d ¼ 2x ð2x_ Þ ¼ 2x þ 2xð2Þ ¼ 0 _ @x dt @x dt
or
xð2Þ þ x ¼ 0
where xð2Þ is the second derivative of x with respect to t. The general solution of the Euler–Lagrange equation is xðtÞ ¼ A1 sin t þ A2 cos t The constants A1 and A2 are determined using the boundary conditions (11.2-10). For the present example, we have (see Case 1 of Remark 11.2.1) ¼1 and xðtf Þ ¼ x xðt0 Þ ¼ xð0Þ ¼ 0 2 Thus xð0Þ ¼ A2 ¼ 0
and
¼ A1 ¼ 1 x 2
Hence, the optimum xðtÞ sought is xðtÞ ¼ sin t. The graphical representation of the optimum xðtÞ is given in Figure 11.1.
Figure 11.1
Graphical representation of the solution of Example 11.2.1.
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Example 11.2.2 Determine a trajectory xðtÞ such as to minimize the distance between the point xðt0 Þ ¼ xð0Þ ¼ 1 and the straight line t ¼ tf ¼ 2. Solution In Figure 11.2 we present a few possible trajectories which may satisfy the problem specifications, since they all start from the point xðto Þ ¼ 1 and end on the straight line t ¼ tf ¼ 2. However, the optimum trajectory sought is the one which will minimize the cost function ð tf ð2 _ J¼ ’½xðtÞ; xðtÞ; tdt ¼ ds 0
tÞ
where ðdsÞ ¼ ðdxÞ þ ðdtÞ and hence ds ¼ ð1 þ x_ 2 Þ1=2 dt. Therefore ð2 1=2 1=2 1 þ x_ 2 dt; where ’ðx; x_ ; tÞ ¼ 1 þ x_ 2 J¼ 2
2
2
0
The Euler–Lagrange equation is @’ d @’ d xð2Þ ð1 þ x_ 2 Þ x_ 2 xð2Þ x_ ¼ ¼ ¼0 1=2 @x dt @x_ dt ð1 þ x_ 2 Þ ð1 þ x_ 2 Þ3=2 The above equation reduces to the differential equation xð2Þ ðtÞ ¼ 0. The general solution of the Euler–Lagrange equation will then be xðtÞ ¼ A1 t þ A2 The constants A1 and A2 are determined by using the boundary conditions (11.2-10). For the present example, the boundary conditions are fixed at t0 ¼ 0 but free at tf ¼ 2 (see Case 2 of Remark 11.2.1). Thus @’ A1 x_ ¼ xð0Þ ¼ A2 ¼ 1 and ¼ ¼0 1=2 2 @x_ t¼2 ð1 þ x_ Þ t¼2 ð1 þ A21 Þ1=2 Hence, the optimum trajectory xðtÞ is the straight line xðtÞ ¼ 1. Example 11.2.3 Determine the optimum trajectory which minimizes the cost function
Figure 11.2
Several possible trajectories for Example 11.2.2.
Optimal Control
J¼
ð tf
487
’½xðtÞ; x_ ðtÞ; t dt ¼
t0
ð2 0
1 2 x_ þ xx_ þ x_ þ x dt 2
where no restrictions are placed upon the optimum xðtÞ at the boundaries t0 ¼ 0 and tf ¼ 2. Solution The Euler–Lagrange equation is given by @’ d @’ d ¼ x_ þ 1 ðx_ þ x þ 1Þ ¼ 1 xð2Þ ¼ 0 @x dt @x_ dt The general solution of the above differential equation is 1 xðtÞ ¼ t2 þ A1 t þ A2 2 The constants A1 and A2 are determined by using the boundary conditions (11.2-10). For the present example, the boundary conditions are free at both t0 ¼ 0 and at tf ¼ 2 (see Case 4 of Remark 11.2.1). We thus have @’ ¼ x_ þ x þ 1 ¼ 0; @x_
for t ¼ 0 and 2
This leads to the following two algebraic system of equations: @’ 1 2 t þ A1 t þ A2 þ 1 ¼ A1 þ A2 þ 1 ¼ 0 ¼ ðt þ A1 Þ þ @x_ 2 t¼0 t¼0 @’ 1 2 t þ A1 t þ A2 þ 1 ¼ 3A1 þ A2 þ 5 ¼ 0 ¼ ðt þ A1 Þ þ @x_ 2 t¼2 t¼2
The solution of these two algebraic equations yields A1 ¼ 2 and A2 ¼ 1. Hence, the optimal trajectory is xðtÞ ¼ 12 t2 2t þ 1. 2 Maxima and Minima of Functionals with Constraints Here, we will extend the results of maxima and minima of functionals without constraints to the more general case where constraint equations are imposed upon the problem of optimization. More specifically, we will study the case where the cost function has the form ð tf tÞ dt J¼ ’ðx; x; ð11:2-16Þ t0
where xðtÞ is constrained by the following set of equations: tÞ ¼ 0 fðx; x; for t 2 ½t0 ; tf
ð11:2-17Þ
To determine the maxima and minima of J under the constraint (11.12-17) we will apply the method of Lagrange multipliers. To this end, define a new cost function J 0 as follows: ð tf ð tf 0 T k; tÞ dt ðx; x; ð11:2-18Þ J ¼ ½’ðx; x; tÞ þ k ðtÞfðx; x; tÞ dt ¼ t0
t0
488
Chapter 11
where k ¼ ð1 ; 2 ; . . . ; n ÞT is the vector of Language multipliers and tÞ k; tÞ ¼ ’ðx; x; tÞ þ kT ðtÞfðx; x; ðx; x; tÞ ¼ 0, it follows that kT ðtÞfðx; x; tÞ ¼ 0 and hence J ¼ J 0 . If we extend Since fðx; x; the results of Remark 11.2.2 to the present case where constraints are involved, we will arrive at the following Euler–Lagrange equation: @ d @ ¼0 ð11:2-19Þ @x dt @ x Clearly, when no constraints are involved, then Eq. (11.2-19) reduces to Eq. (11.2-14) of Remark 11.2.2. Example 11.2.4 Determine the optimum trajectory xðtÞ which minimizes the cost function ð1 ð tf 1 2 u ðtÞ dt ’½uðtÞ; t dt ¼ J¼ 02 t0 where xðtÞ is subject to the constraint tÞ ¼ Ax þ bu x ¼ 0 x ¼ Ax þ bu or fðx; x; where
x1 x¼ ; x2
0 1 A¼ ; 0 0
with boundary conditions 1 x1 ð0Þ ¼ xð0Þ ¼ x2 ð0Þ 1
and
0 b¼ 1
and
xð1Þ ¼
x1 ð1Þ 0 ¼ x2 ð1Þ 0
Solution The present problem is the minimum effort problem defined in Eq. (11.1-5), subject to the constraints of the system model, namely, the state-space equations x ¼ Ax þ bu. Apply the method of Lagrange multipliers to yield ð1 ð1 1 2 0 T T u þ k ðAx þ bu xÞ dt J ¼ ½’ðu; tÞ þ k ðtÞfðx; x; tÞ dt ¼ 0 0 2 ð1 ð1 1 2 k; tÞ dt u þ 1 ðx2 x_ 1 Þ þ 2 ðu x_ 2 Þ dt ¼ ðu; x; x; ¼ 0 2 0 where kT ðtÞ ¼ ½1 ðtÞ; 2 ðtÞ is the Lagrange multiplier vector and k; tÞ ¼ 1 u2 þ ðx x_ Þ þ ðu x_ Þ ðu; x; x; 1 2 1 2 2 2 In what follows, we will determine simultaneously both the optimum xðtÞ and the optimum uðtÞ. This means that we must determine the three functions x1 ðtÞ, x2 ðtÞ, and uðtÞ. The Euler–Lagrange equation (11.2-19) is actually the following three differential equations:
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489
@ d @ ¼ _1 ¼ 0 @x1 dt @x_ 1 @ d @ ¼ 1 þ _2 ¼ 0 @x2 dt @x_ 2 @ d @ ¼ u þ 2 ¼ 0 @u dt @u_ The general solution of these three equations is 1 ðtÞ ¼ A1 ;
2 ðtÞ ¼ A1 t þ A2 ;
and
uðtÞ ¼ 2 ðtÞ ¼ A1 t A2
Note that from the state-space system model we have x_ 1 ¼ x2 x_ 2 ¼ uðtÞ Therefore x_ 2 ¼ A1 t A2 . Hence ðt ðt 1 x2 ðtÞ ¼ uðtÞ dt ¼ ðA1 t A2 Þ dt ¼ A1 t2 A2 t þ A3 2 0 0 and
ðt x1 ðtÞ ¼
1 1 x2 ðtÞ dt ¼ A1 t3 A2 t2 þ A3 t þ A4 6 2 0
The constants A1 , A2 , A3 , and A4 will be determined using the boundary conditions. We have x1 ð0Þ ¼ A4 ¼ 1 x2 ð0Þ ¼ A3 ¼ 1 1 1 x1 ð1Þ ¼ A1 A2 þ A3 þ A4 ¼ 0 6 2 1 x2 ð1Þ ¼ A1 A1 þ A3 ¼ 0 2 This system of four algebraic equations has the following solution: A1 ¼ 18, A2 ¼ 10, A3 ¼ 1, and A4 ¼ 1. Hence, the optimum xðtÞ is given by 3 x1 ðtÞ 3t 5t2 þ t þ 1 xðtÞ ¼ ¼ x2 ðtÞ 9t2 10t þ 1 while the optimum uðtÞ is given by uðtÞ ¼ 18t 10 Note that here the final system is an open-loop system. 11.2.2 The Maximum Principle The method of calculus of variations, presented in Subsec. 11.2.1, constitutes a general methodology for the study of maxima and minima of a functional. Here, we will restrict our interest to specialized optimization methods which facilitate the solution of optimal control design problems. Such a method is the maximum prin-
490
Chapter 11
ciple which was initially proposed by Pontryagin [17]. This method is based on the calculus of variations and yields a general solution to optimal control problems. More specifically, the following general control problem will be studied. Consider the cost function tf ð tf J ¼ ðx; tÞ þ ’ðx; u; tÞ dt ð11:2-20Þ t0
t0
Determine the optimum control vector uðtÞ which minimizes the cost function J, where the system under control is described by a mathematical model in state space having the general form x ¼ fðx; u; tÞ ð11:2-21Þ To solve the problem, apply the Lagrange multipliers method. To this end, define the new cost function J 0 as follows: tf ð t f 0 dt ’ðx; u; tÞ þ kT ðtÞ½fðx; u; tÞ x ð11:2-22Þ J ¼ ðx; tÞ þ t0
t0
0
Clearly J ¼ J . To facilitate the study of the new cost function J 0 , we introduce the Hamiltonian function, defined as Hðx; u;k; tÞ ¼ ’ðx; u; tÞ þ kT x ¼ ’ðx; u; tÞ þ kT fðx; u; tÞ
ð11:2-23Þ
where k is the vector of Lagrange multipliers. If we substitute Eq. (11.2-23) in Eq. (11.2-22), we have tf ð t f dt ð11:2-24Þ J 0 ¼ ðx; tÞ þ ½Hðx; u;k; tÞ k_ T x t0
t0
If we apply the integration by parts method, Eq. (11.2-24) becomes tf ð t f J 0 ¼ ½ðx; tÞ kT x þ ½Hðx; u; k; tÞ k_ T x dt t0
ð11:2-25Þ
t0
The first differential J 0 with respect to the vectors x and u is given by tf ð tf 0 T @ T @H T @H _ k þ k þ u þ x J ¼ x dt @x @x @u t0 t0
ð11:2-26Þ
Using Theorem 11.2.1, it follows that a necessary condition for J 0 to be maximum or minimum is that J 0 ¼ 0. Application of this theorem in Eq. (11.2-26) yields that for J 0 to be zero, for every x and u, the vectors x and u must satisfy the equations @H ¼ k_ @x
ð11:2-27aÞ
@H ¼0 @u
ð11:2-27bÞ
@H ¼ x ¼ fðx; u; tÞ @k
ð11:2-27cÞ
with boundary conditions
Optimal Control
xT
491
@ k ¼ 0; @x
for t ¼ t0 and tf
ð11:2-27dÞ
The first three equations (Eqs (11.2-27a,b,c)) are of paramount importance in control engineering and they are called canonical Hamiltonian equations. Now, consider the second differential 2 J 0 of the cost function J 0 . We have " # tf " # ð 2 x 1 1 tf T .. T 2 0 T@ x x þ ½x . u P dt J ¼ ð11:2-28Þ 2 2 t0 @x2 u t0 where P is an ðn þ mÞ ðn þ mÞ square matrix having the form 3 2 @2 H @ @H 6 @u @x 7 @x2 6 7 P ¼ 6 7 T 4 @ @H @2 H 5 @u @x
@u2
where use was made of the first differential of Eq. (11.2-21), i.e., use was made of the relation x ¼
@f @f x þ u @x @u
ð11:2-29Þ
For J 0 (and hence for J, since J 0 ¼ J) to be minimum, the matrices P and @2 h=@x2 must be negative definite (see Sec. 2.12). Remark 11.2.4 It has been shown that the control signal uðtÞ which minimizes the cost function J, necessarily minimizes the Hamiltonian function, i.e., it holds that Hðx; u; k; tÞ Hðx; u~ ; k; tÞ where u~ is any control signal, different from the optimum control signal u. For this reason, the present method is known as the minimum principle method. However, because of a sign difference in the Hamiltonian function, the method has become known as the maximum principle. Example 11.2.5 Minimize the cost function ð1 1 2 u ðtÞ dt J¼ 02 when the system under control is the following: 0 1 x1 0 x_ 1 ¼ þ u 0 1 x2 1 x_ 2 with boundary conditions xð0Þ ¼ 0
and
xð1Þ ¼ ½4
2T
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Chapter 11
Solution For the present problem the Hamiltonian function has the form H ¼ 12 u2 þ kT ðAx þ buÞ ¼ 12 u2 þ 1 x2 2 x2 þ 2 u The canonical Hamiltonian equations are 2 3 @H " # " # 7 0 _1 @H 6 6 @x1 7 ¼6 ¼ 7¼ @x 4 @H 5 1 2 _2 @x2 @H ¼ u þ 2 ¼ 0 @u " # " # x_ 1 x2 @H ¼ ¼ @k x2 þ u x_ 2 The general solution of the above equations is given by 1 ðtÞ ¼ C3 2 ðtÞ ¼ C3 ½1 et þ C4 et
x1 ðtÞ ¼ C1 þ C2 ½1 et þ C3 t 12 et þ 12 et þ C4 ½1 12 et 12 et
x2 ðtÞ ¼ C2 et þ C3 1 þ 12 et þ 12 et þ C4 12 et 12 et The parameters C1 , C2 , C3 , and C4 are determined using the boundary conditions. We have x1 ð0Þ ¼ C1 ¼ 0, x2 ð0Þ ¼ C2 ¼ 0, x1 ð1Þ ¼ 4, and x2 ð1Þ ¼ 2. From these algebraic equations we readily have that C1 ¼ 0, C2 ¼ 0, C3 ¼ 40:5, and C4 ¼ 20:42. Hence x1 ðtÞ ¼ 40:5t 20:42 þ 30:46et 10:04et x2 ðtÞ ¼ 40:5 30:46et 10:04et and the optimum control signal uðtÞ is given by uðtÞ ¼ 2 ðtÞ ¼ 40:5 20:08et Note that here the final system is an open-loop system. 11.3 11.3.1
OPTIMAL LINEAR REGULATOR General Remarks
The optimal linear regulator problem is a special, but very important, optimal control problem. Simply speaking, the regulator problem can be stated as follows. Consider a linear homogeneous system with zero input and nonzero initial state vector conditions xðt0 Þ. Here, the vector xðt0 Þ is the only excitation to the system. An optimal control signal uðtÞ is to be determined such as to restore the state vector to its equilibrium point, i.e., such that xðtf Þ ’ 0, while minimizing a certain cost function.
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493
As a practical example of an optimal regulator, consider a ground antenna having a fixed orientation. Assume that the antenna undergoes a disturbance, e.g., due to a sudden strong wind. As a result, the antenna will be forced to a new position xðt0 Þ. It is obvious that in the present situation it is desirable to implement a control strategy which will restore the antenna to its equilibrium position. Furthermore, this restoration must take place in the time interval ½t0 ; tf , while minimizing a certain cost function. This cost function normally includes the following three characteristics: a.
The amplitude of the optimal control signal uðtÞ should be as small as possible, making the required control effort (control energy) for restoring the antenna to its equilibrium position as small as possible. b. The amplitude of xðtÞ should be small enough to avoid saturations or even damage (i.e., from overheating) to the system under control. c. The final value xðtf Þ of xðtÞ should be as close as possible to the equilibrium point of the system, i.e., xðtf Þ ’ 0. Another practical example of an optimal regulator is the problem of ship stabilization presented in Figure 1.20 of Chapter 1 and in Example 6.4.11 of Chapter 6. From a mathematical point of view, the optimal regulator problem may be formulated as follows. Consider the linear, time-varying system described in state space by the vector differential equation ¼ AðtÞxðtÞ þ BðtÞuðtÞ; xðtÞ xðt Þ ¼ x ð11:3-1Þ 0
0
Find a control signal uðtÞ which minimizes the cost function ð 1 1 tf T J ¼ xT ðtf ÞSxðtf Þ þ ½x ðtÞQðtÞxðtÞ þ uT ðtÞRðtÞuðtÞ dt 2 2 t0
ð11:3-2Þ
The foregoing cost function J is identical to the cost function (11.1-9). This criterion is a sum of inner products of the vectors xðtÞ and uðtÞ, and for this reason it is called the quadratic cost function. The matrices S, QðtÞ, and RðtÞ are weighting matrices and are chosen to be symmetric. Here, we stress again that the main reason for including the energy-like quadratic terms xT ðtÞQðtÞxðtÞ and uT ðtÞRðtÞuðtÞ in the cost function J is to minimize the dissipated energy in the system and the required input energy (control effort), respectively. The quadratic term xT ðtf ÞSxðtf Þ is included in J to force the final value xðtf Þ of xðtÞ to be as close as possible to the equilibrium point of the system. Note that xðtf Þ is unspecified. 11.3.2 Solution of the Optimal Linear Regulator Problem The minimization of the cost function J will be done using the method of maximum principle. To this end, define the Hamiltonian Hðx; u; k; tÞ ¼ 12 xT ðtÞQðtÞxðtÞ þ 12 uT ðtÞRðtÞuðtÞ þ kT ðtÞ½AðtÞxðtÞ þ BðtÞuðtÞ ð11:3-3Þ where kðtÞ is the vector of the Lagrange multipliers. Next, define the new cost criterion J 0 ¼ J by adding the zero term kT ðtÞ½AðtÞxðtÞ þ BðtÞuðtÞ xðtÞ to the initial cost function J as follows:
494
Chapter 11
J0 ¼
1 T 1 x ðtf ÞSxðtf Þ þ 2 2
ð tf
½xT ðtÞQðtÞxðtÞ þ uT ðtÞRðtÞuðtÞ
t0
dt þ kT ðtÞ½AðtÞxðtÞ þ BðtÞuðtÞ xðtÞ or 1 J ¼ xT ðtf ÞSxðtf Þ þ 2 0
ð tf
dt ½Hðx; u;k; tÞ kT ðtÞ xðtÞ
t0
where use was made of the Hamiltonian defined in Eq. (11.3-3). Using the method of integration by parts, the cost criterion J 0 becomes ð tf
tf 1 J 0 ¼ xT ðtf ÞSxðtf Þ kT ðtÞxðtÞ t þ ½Hðx; u; k; tÞ k_ T ðtÞxðtÞ dt 0 2 t0 The first differential J 0 with respect to the vectors x and u is given by ð tf @H _ @H þ k þ uT J 0 ¼ Sxðtf Þ kðtf Þ þ dt xT @x @u t0 where use was made of the fact that xðt0 Þ is fixed and that xðtf Þ is unspecified. It has been proven that a necessary condition for J to be maximum or minimum is that J 0 ¼ 0 (see Theorem 11.2.1). Consequently, the vectors x and u should satisfy the equation J 0 ¼ 0, in which case the following relations should hold: @H ¼ k_ ðtÞ ¼ QðtÞxðtÞ þ AT ðtÞkðtÞ @x
ð11:3-4aÞ
@H ¼ 0 ¼ RðtÞuðtÞ þ BT ðtÞkðtÞ @u
ð11:3-4bÞ
@H ¼ AðtÞxðtÞ þ BðtÞuðtÞ @k @ ¼ Sxðtf Þ ¼ kðtf Þ @x
ð11:3-4cÞ ð11:3-4dÞ
t¼tf
where use was made of the following vector and matrix properties (see relations (2.617) and (2.6-18) of Chapter 2): @ T ½q ðtÞxðtÞ ¼ qðtÞ @x
and
1 @ T ½x ðtÞQðtÞxðtÞ ¼ QðtÞxðtÞ 2 @x
where QðtÞ is a symmetric matrix. As first pointed out in Subsec. 11.2.2, Eqs. (11.34a,b,c) are called canonical Hamiltonian equations and relation (11.3-4d) represents the boundary conditions of the problem. Note that for the present case Eq. (11.3-4d) refers only to the final condition, i.e., for t ¼ tf . In the sequel, we will solve the canonical Hamiltonian equations (11.3-a,b,c) with respect to uðtÞ. This solution must satisfy the boundary condition (11.3-4d). For simplicity, assume that RðtÞ is invertible, i.e., jRðtÞj 6¼ 0 for every t 2 ½t0 ; tf . Thus, from relation (11.3-4b), we obtain uðtÞ ¼ R1 ðtÞBT ðtÞkðtÞ
ð11:3-5Þ
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495
At this point we make the assumption that the solution of Eq. (11.3-5) can be expressed as a linear state feedback law, i.e., we assume that uðtÞ ¼ KðtÞxðtÞ
ð11:3-6Þ
where KðtÞ is called the state feedback matrix. We also assume that the vector of Lagrange multipliers kðtÞ is linear in xðtÞ, i.e., we assume that kðtÞ ¼ PðtÞxðtÞ
ð11:3-7Þ
Note that the vector of Lagrange multipliers kðtÞ is called the costate vector. If we substitute Eq. (11.3-5) in Eq. (11.3-1), we have ¼ AðtÞxðtÞ BðtÞR1 ðtÞBT ðtÞkðtÞ xðtÞ
ð11:3-8aÞ
If we substitute Eq. (11.3-7) in Eq. (11.3-8a), we obtain ¼ AðtÞxðtÞ BðtÞR1 ðtÞBT ðtÞPðtÞxðtÞ xðtÞ If we differentiate Eq. (11.3-7), we have ¼ QðtÞxðtÞ AT ðtÞkðtÞ kðtÞ ¼ PðtÞxðtÞ þ PðtÞ xðtÞ
ð11:3-8bÞ
ð11:3-9Þ
where use was made of Eq. (11.3-4a). Finally, if we substitute Eq. (11.3-8b) in Eq. (11.3-9) and use Eq. (11.3-7), we arrive at the relation ½ PðtÞ PðtÞAðtÞ þ AT ðtÞPðtÞ þ QðtÞ PðtÞBðtÞR1 ðtÞBT ðtÞPðtÞxðtÞ ¼ 0 ð11:3-10Þ Relation (11.3-10) must hold for all vectors xðtÞ 6¼ 0. For this to be valid the coefficient of xðtÞ must be equal to zero, i.e., PðtÞ þ PðtÞAðtÞ þ AT ðtÞPðtÞ PðtÞBðtÞR1 ðtÞBT ðtÞPðtÞ ¼ QðtÞ ð11:3-11Þ Relation (11.3-11) is known as the matrix Riccati differential equation where the matrix PðtÞ is unknown. The final condition of matrix PðtÞ, according to relation (11.3-4d) and definition (11.3-7), will be Sxðtf Þ ¼ kðtf Þ ¼ Pðtf Þxðtf Þ Consequently Pðtf Þ ¼ S
ð11:3-12Þ
If we substitute relation (11.3-7) in relation (11.3-5), we obtain uðtÞ ¼ R1 ðtÞBT ðtÞPðtÞxðtÞ
ð11:3-13Þ
By comparing relations (11.3-13) and (11.3-6), we have KðtÞ ¼ R1 ðtÞBT ðtÞPðtÞ
ð11:3-14Þ
Henceforth, the optimal solution of the linear optimal regulator problem is of the form (11.3-6), where the matrix KðtÞ is given by relation (11.3-14). To determine the feedback matrix KðtÞ one has to solve the Ricatti equation (11.3-11) for PðtÞ. The solution (11.3-13) was first determined by Kalman, and it is for this reason that matrix KðtÞ is called the Kalman matrix [8]. The second differential 2 J of the cost function J is given by
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Chapter 11
1 1 2 J ¼ dxT ðtf ÞSdxðtf Þ þ 2 2
ð tf
dxT ðtÞQðtÞdxðtÞ þ duT ðtÞRðtÞduðtÞ dt
ð11:3-15Þ
t0
For the cost function J to be minimal it must hold that 2 J 0. We observe that 2 J is a sum of three terms which are in quadratic form. Consequently for 2 J 0 to hold true, every term in Eq. (11.3-15) must be positive definite. Thus, for Eq. (11.313) to be the solution of the problem, the matrices S, QðtÞ, and RðtÞ should be at least positive semidefinite matrices. The following theorems hold true for the Ricatti equation (11.3-11). Theorem 11.3.1 If S is positive definite and QðtÞ is at least nonnegative definite, or vice versa, and RðtÞ is positive definite, then a minimum J exists if and only if the solution PðtÞ of the Riccati equation (11.3-11) exists, is bounded, and is positive definite for all t < tf . Under these conditions the minimum cost function J becomes J ¼ 12 xT ðt0 ÞPðt0 Þxðt0 Þ
Theorem 11.3.2 If S, QðtÞ, and RðtÞ are symmetric, then the solution of the Riccati equation (11.3-11) is also a symmetric matrix. This means that in this case the n n matrix PðtÞ has nðn þ 1Þ=2 unknown elements and, consequently, the solution of eq. (11.3-11) requires only the solution of nðn þ 1Þ=2 equations. Two block diagrams referring to the problem of the optimal regulator and its solution are given in Figures 11.3 and 11.4. The Ricatti equation (11.3-11) is usually solved using a digital computer rather than analytically. Since the final condition Pðtf Þ is given, the solution using a digital computer is carried out starting from the final point t ¼ tf and going backwards until we reach the starting part t ¼ t0 . However, this can be avoided if we change variables in the following way. Let ¼ tf t. Then the Ricatti equation becomes
Figure 11.3
A simplified block diagram of the optimal linear regulator.
Optimal Control
Figure 11.4
497
Block diagram of the optimal linear regulator, using the Riccati equation.
dPðtf Þ Pðtf ÞAðtf Þ AT ðtf ÞPðtf Þ d þ Pðtf ÞBðtf ÞR1 ðtf ÞBT ðtf ÞPðtf Þ ¼ Qðtf Þ ð11:3-16Þ with initial condition Pð0Þ ¼ S. Equation (11.3-16) is solved using a digital computer by starting at point ¼ 0 and ending at point ¼ tf t0 . Remark 11.3.1 Another method of determining the optimal control vector uðtÞ is the following. Rewrite relations (11.3-8a) and (11.3-4a) in the form xðtÞ AðtÞ BðtÞR1 ðtÞBT ðtÞ xðtÞ ¼ ð11:3-17Þ kðtÞ kðtÞ QðtÞ AT ðtÞ with boundary conditions xðt0 Þ ¼ x0 and kðtf Þ ¼ Sxðtf Þ. The solution of Eq. (11.3-17) yields the vector kðtÞ, on the basis of which the optimal control vector uðtÞ may be calculated using relation (11.3-5). To determine kðtÞ, we work as follows. The solution of Eq. (11.3-17) has the general form xðtÞ xðtf Þ ð11:3-18Þ ¼ rðtf ; tÞ kðtf Þ kðtÞ
498
Chapter 11
where the 2n 2n matrix rðtf ; tÞ is the transition matrix of Eq. (11.3-17). Partition Eq. (11.3-18) as follows: r11 ðtf ; tÞ r12 ðtf ; tÞ xðtÞ xðtf Þ ¼ ð11:3-19Þ kðtf Þ r21 ðtf ; tÞ r22 ðtf ; tÞ kðtÞ where all four submatrices r11 , r12 , r21 , and r22 have dimensions n n. Since, according to Eq. (11.3-4d), we have that kðtf Þ ¼ Sxðtf Þ, relation (11.3-19) is written as xðtf Þ ¼ r11 ðtf ; tÞxðtÞ þ r12 ðtf ; tÞkðtÞ Sxðtf Þ ¼ r21 ðtf ; tÞxðtÞ þ r22 ðtf ; tÞkðtÞ These two equations yield kðtÞ ¼ PðtÞxðtÞ
ð11:3-20aÞ
where PðtÞ ¼ ½r22 ðtf ; tÞ Sr12 ðtf ; tÞ1 ½Sr11 ðtf ; tÞ r21 ðtf ; tÞ
ð11:320bÞ
Finally uðtÞ ¼ R1 ðtÞBT ðtÞPðtÞxðtÞ ¼ KðtÞxðtÞ
ð11:3-21aÞ
where KðtÞ ¼ R1 ðtÞBT ðtÞ½r22 ðtf ; tÞ Sr12 ðtf ; tÞ1 ½Sr11 ðtf ; tÞ r21 ðtf ; tÞ ð11:3-21bÞ The present method is easily applied when the matrices A, B, Q, and R are time invariant. In this case the transition matrix rðtÞ is the inverse Laplace transform of the matrix 1 A BR1 BT rðsÞ ¼ sI ð11:3-22Þ Q AT where t has been replaced by tf t. When the matrices A, B, Q, and R are time varying, the transition matrix is usually computed numerically. 11.3.3
The Special Case of Linear Time-Invariant Systems
Consider the special case of linear time-invariant systems. Furthermore, assume that the weighting matrix S ¼ 0 and that tf ! þ1. Then the matrix PðtÞ and, consequently, the matrix KðtÞ, are time invariant and the Riccati equation reduces to the nonlinear algebraic equation PA þ AT P PBR1 BT P ¼ Q
ð11:3-23Þ
The following interesting and very useful results have been proven concerning the solution of Eq. (11.3-23): 1. 2. 3.
If there exists a matrix P, it is positive definite and unique. If there exists a matrix P, the closed-loop system is asymptotically stable. If i is an eigenvalue of matrix M, then i is also an eigenvalue of matrix M, where A BR1 BT M¼ ð11:3-24Þ Q AT
Optimal Control
4.
499
If matrix M has n distinct eigenvalues 1 ; 2 ; . . . ; n with Re i < 0, then P ¼ GT1
ð11:3-25Þ
where . . . G ¼ ½g1 .. g2 .. .. gn
and
. . . T ¼ ½t1 .. t2 .. .. tn
where the vectors gi and ti are defined as t i xi ¼ Mxi ; i ¼ 1; 2; . . . ; n xi ¼ i ; gi 5.
That is, the vector xi is an eigenvector of the matrix M. The state vector xðtÞ of the closed-loop system is given by xðtÞ ¼ Te,ðtt0 Þ T1 x0 ;
6.
1
, ¼ diagð1 ; 2 ; . . . ; n Þ
ð11:3-26Þ
T
The matrix A BR B P of the closed-loop system has eigenvalues 1 ; 2 ; . . . ; n and eigenvectors t1 ; t2 ; . . . ; tn .
Example 11.3.1 Consider the scalar system x_ ðtÞ ¼ uðtÞ;
xð0Þ ¼ 1
with cost function ð1 ½x2 ðtÞ þ u2 ðtÞ dt J¼ 0
Thus, here we have S ¼ 0, QðtÞ ¼ 2, and RðtÞ ¼ 2. Find the optimal uðtÞ and xðtÞ, both in the form an open-loop system as well as in the form of a closed-loop system. Solution First we study the case of the open-loop system using relation (11.3-17) of Remark 11.3.1. For the present example, we have 0 0:5 xðtÞ x_ ðtÞ ; xð0Þ ¼ 1; and ð1Þ ¼ Sxð1Þ ¼ 0 ¼ 2 0 ðtÞ _ðtÞ From this vector differential system, we have that x_ ¼ 0:5 and _ ¼ 2x. Thus xð2Þ ¼ x, where xð2Þ is the second derivative of xðtÞ with respect to t. The solution of this last differential equation is xðtÞ ¼ Aet þ Bet and, hence, ðtÞ ¼ 2Aet 2Bet . From the boundary conditions xð0Þ ¼ 1 and ðþ1Þ ¼ 0, we have that A ¼ 1 and B ¼ 0. Therefore ðtÞ ¼ 2et and, consequently, the optimal uðtÞ and xðtÞ are uðtÞ ¼ et
and
xðtÞ ¼ et
Consequently, for the cost function to be minimal we must excite the system with the input uðtÞ ¼ et . This input can be produced, for example, by a waveform generator. In this case, of course, we have an open-loop system. For the case of the closed-loop system we solve the algebraic Riccati equation, which for the present example is 0:5p2 ¼ 2. Consequently, p ¼ 2. Since p must be positive definite we keep only the value p ¼ 2. Thus, the optimal uðtÞ and xðtÞ are
500
Chapter 11
uðtÞ ¼ xðtÞ
xðtÞ ¼ et
and
We observe that the optimal xðtÞ is, as expected, the same with that of the open-loop system. We also observe that the optimal uðtÞ has not a specific waveform, as in the case of the open-loop system, but it is a function of xðtÞ. Example 11.3.2 Consider the scalar system x_ ðtÞ ¼ uðtÞ;
xðt0 Þ ¼ x0
with cost function 1 1 J ¼ sx2 ðtf Þ þ 2 2
ð tf
u2 ðtÞ dt
t0
Find the optimal uðtÞ as a function of xðtÞ. Solution The Hamiltonian is given by H ¼ 12 u2 þ u Hence, the canonical equations are @H ¼ _ ¼ 0; @x @H ¼ u þ ¼ 0; @u @H ¼ x_ ¼ u @
thus ¼ C ¼ constant thus u ¼ ¼ C
with boundary conditions ðtf Þ ¼ sxðtf Þ. The Riccati equation is given by p_ p2 ¼ 0;
pðtf Þ ¼ s
The Ricatti equation may be written as follows: dp ¼ dt p2 Integrating from t to tf we have 1 tf ¼ tf t and hence p t
1 pðtÞ ¼ 1 xðtÞ s þ tf t
Consequently, the optimal uðtÞ becomes 1 xðtÞ uðtÞ ¼ pðtÞxðtÞ ¼ 1 s þ tf t Example 11.3.3 Consider the scalar system x_ ðtÞ ¼ axðtÞ þ buðtÞ;
xðt0 Þ ¼ x0
Optimal Control
501
with cost function 1 1 J ¼ sx2 ðtf Þ þ 2 2
ð tf
u2 ðtÞ dt
t0
Find the optimal uðtÞ as a function of xðtÞ. Solution The Hamiltonian is given by H ¼ 12 u2 þ ðax þ buÞ Hence, the canonical equations are @H ¼ _ ¼ a @x @H ¼ u þ b @u @H ¼ x_ ¼ ax þ bu @ with boundary conditions ðtf Þ ¼ sxðtf Þ. The Riccati equation is p_ 2ap b2 p2 ¼ 0;
pðtf Þ ¼ s
and may be written as b2 p_ ¼ 2ap þ b p ¼ 2a p þ p2 2a
!
2 2
or b2 dp dp 22a ¼ 2adt p b pþ1 2a Integrating from t to tf , we have ! " # b2 b2 ln s ln pðtÞ ln s þ 1 þ ln pðtÞ þ 1 ¼ 2aðtf tÞ 2a 2a or
3 2 2 b þ 1 s 7 6 2a b 7 þ 2aðtf tÞ ln pðtÞ þ 1 ln pðtÞ ¼ ln6 4 s 5 2a "
or
2
#
2 2 3 b2 b þ 1 6s þ 17 2a 2a 7 ¼6 4 s 5 exp½2aðtf tÞ pðtÞ
pðtÞ
502
Chapter 11
Solving for pðtÞ, we obtain pðtÞ ¼
exp½2aðtf tÞ
1 b þ 1 exp½2aðtf tÞ s 2a 2
Consequently, the optimal uðtÞ becomes 2
3
6 7 exp½2aðtf tÞ 7xðtÞ uðtÞ ¼ bpðtÞxðtÞ ¼ b6 41 b2
5 þ 1 exp½2aðtf tÞ s 2a 11.4
OPTIMAL LINEAR SERVOMECHANISM OR TRACKING PROBLEM
Consider the linear time-varying system ¼ AðtÞxðtÞ þ BðtÞuðtÞ xðtÞ yðtÞ ¼ CðtÞxðtÞ
ð11:4-1aÞ ð11:4-1bÞ
Let mðtÞ be the desired closed-loop system output vector. Then the optimal linear servomechansim or tracking problem may be stated as follows: determine a control vector uðtÞ such that the cost function ð
1 T 1 tf T J ¼ e ðtf ÞSeðtf Þ þ e ðtÞQðtÞeðtÞ þ uT ðtÞRðtÞuðtÞ dt ð11:4-2Þ 2 2 t0 is minimized, where eðtÞ ¼ mðtÞ yðtÞ ¼ mðtÞ CxðtÞ. Clearly, the optimal linear servomechanism problem is a generalization of the optimal linear regulator problem. The solution of the optimal servomechanism problem will be determined using the same technique which was applied for the solution of the optimal regulator problem. Thus, we start with the Hamiltonian, defined as follows: Hðx; u; k; tÞ ¼ 12 eT ðtÞQðtÞeðtÞ þ 12 uT ðtÞRðtÞuðtÞ þ kT ðtÞ½AðtÞxðtÞ þ BðtÞuðtÞ ð11:4-3Þ The canonical equations are @H ¼ kðtÞ ¼ CT ðtÞQðtÞ½CðtÞxðtÞ mðtÞ þ AT ðtÞkðtÞ @x @H ¼ 0 ¼ RðtÞuðtÞ þ BT ðtÞkðtÞ @u @H ¼ AðtÞxðtÞ þ BðtÞuðtÞ @k and the final condition
@ 1 T kðtf Þ ¼ e ðtf ÞSeðtf Þ ¼ CT ðtf ÞS Cðtf Þxðtf Þ mðtf Þ @x 2 where use was made of the following vector and matrix properties:
ð11:4-4aÞ ð11:4-4bÞ ð11:4-4cÞ
ð11:4-4dÞ
Optimal Control
@ T q ðtÞxðtÞ ¼ qðtÞ @x
503
" #
1 @ T @zT ðtÞ z ðtÞEðtÞzðtÞ ¼ EðtÞzðtÞ 2 @x @x
and
where EðtÞ is a symmetric matrix. From relation (11.4-4b) and provided that jRðtÞj 6¼ 0 for t 2 ½t0 ; tf , we have uðtÞ ¼ R1 ðtÞBT ðtÞkðtÞ
ð11:4-5Þ
Assume that kðtÞ has the form kðtÞ ¼ PðtÞxðtÞ lðtÞ
ð11:4-6Þ
Then, the control vector (11.4-5) becomes uðtÞ ¼ R1 ðtÞBðtÞ½PðtÞxðtÞ lðtÞ
ð11:4-7Þ
Finally uðtÞ ¼ KðtÞxðtÞ þ qðtÞ where KðtÞ ¼ R1 ðtÞBT ðtÞPðtÞ
and
qðtÞ ¼ R1 ðtÞBT ðtÞlðtÞ
If we substitute Eq. (11.4-5) in Eq. (11.4-1), we obtain ¼ AðtÞxðtÞ BðtÞR1 ðtÞBT ðtÞPðtÞxðtÞ þ BðtÞR1 ðtÞBT ðtÞlðtÞ xðtÞ yðtÞ ¼ CðtÞxðtÞ
ð11:4-8aÞ ð11:4-8bÞ
Differentiate Eq. (11.4-6) to yield lðtÞ kðtÞ ¼ PðtÞxðtÞ þ PðtÞ xðtÞ ¼ CT ðtÞQðtÞ½CðtÞxðtÞ mðtÞ AT ðtÞkðtÞ
¼ CT ðtÞQðtÞCðtÞ þ AT ðtÞPðtÞ xðtÞ þ CT ðtÞQðtÞmðtÞ þ AT ðtÞlðtÞ
ð11:4-9Þ
where use was made of relations (11.4-4a) and (11.4-6). Substituting Eq. (11.4-8a) in Eq. (11.4-9) gives
PðtÞ þ PðtÞAðtÞ PðtÞBðtÞR1 ðtÞBT ðtÞPðtÞ þ CT ðtÞQðtÞCðtÞ þ AT ðtÞPðtÞ xðtÞ
T
þ lðtÞ AðtÞ BðtÞR1 ðtÞBT ðtÞPðtÞ lðtÞ CT ðtÞQðtÞmðtÞ ¼ 0 ð11:4-10Þ For Eq. (11.4-10) to be valid, the coefficient of xðtÞ and the second term in Eq. (11.410) must simultaneously be equal to zero. This reduces Eq. (11.4-10) to the following two differential equations, together with their corresponding final conditions: PðtÞ þ PðtÞAðtÞ þ AT ðtÞPðtÞ PðtÞBðtÞR1 ðtÞBT ðtÞPðtÞ ¼ CT ðtÞQðtÞCðtÞ ð11:4-11aÞ with final condition Pðtf Þ ¼ CT ðtf ÞSCðtf Þ
ð11:4-11bÞ
þ AðtÞ BðtÞR1 ðtÞBT ðtÞPðtÞ T lðtÞ ¼ CT ðtÞQðtÞmðtÞ lðtÞ
ð11:4-12aÞ
and
504
Chapter 11
with final condition lðtf Þ ¼ CT ðtf ÞSmðtf Þ
ð11:4-12bÞ
Consequently, we note that for the determination of the optimal control law (11.4-7) for the linear servomechanism problem it is required to solve two matrix differential equations: the Riccati equation (11.4-11), which is in fact essentially the same as the Riccati equation (11.3-11) of the linear regulator problem; and Eq. (11.4-12), which provides the part of the control vector uðtÞ which depends on the desired output mðtÞ of the closed-loop system. If mðtÞ ¼ 0 and CðtÞ ¼ I, then Eq. (11.4-12) yields lðtÞ ¼ 0 and Eq. (11.4-11) becomes identical to Eq. (11.3-11). This means that in this case the linear servomechanism problem reduces to the linear regulator problem. Remark 11.4.1 Equation (11.4-8a), together with the canonical equation (11.4-4a), may be written as follows: xðtÞ 0 AðtÞ BðtÞR1 ðtÞBT ðtÞ xðtÞ ¼ þ kðtÞ kðtÞ CT ðtÞQðtÞmðtÞ AT ðtÞ CT ðtÞQðtÞCðtÞ ð11:4-13Þ If we set CðtÞ ¼ I and mðtÞ ¼ 0 in Eq. (11.4-13), we obtain Eq. (11.4-17). The solution of Eq. (11.4-13) is given by ð tf xðtf Þ xðtÞ 0 þ rðtf ; Þ T ¼ rðtf ; tÞ d ð11:4-14Þ ðtÞ C ðÞQðÞmðÞ kðtf Þ t Partition the matrix rðtf ; tÞ, as in the case of rðtf ; tÞ of the linear regulator problem in relation (11.3-19), and define ð tf 0 f ðtÞ rðtf ; Þ T d ¼ 1 C ðÞQðÞmðÞ f 2 ðtÞ t Then Eq. (11.4-14) may be rewritten as follows: xðtf Þ ¼ r11 ðtf ; tÞxðtÞ þ r12 ðtf ; tÞkðtÞ þ f 1 ðtÞ
ð11:4-15aÞ
kðtf Þ ¼ r21 ðtf ; tÞxðtÞ þ r22 ðtf ; tÞkðtÞ þ f 2 ðtÞ
ð11:4-15bÞ
with boundary condition
kðtf Þ ¼ CT ðtf ÞS Cðtf Þxðtf Þ mðtf Þ
ð11:4-16Þ
Substituting Eq. (11.4-16) in Eq. (11.4-15b) and xðtf Þ from Eq. (11.4-15a) in Eq. (11.4-15b) and finally solving for kðtÞ, we obtain kðtÞ ¼ PðtÞxðtÞ lðtÞ
ð11:4-17Þ
where
1 T
C ðtf ÞSCðtf Þr11 ðtf ; tÞ r21 ðtf ; tÞ PðtÞ ¼ r22 ðtf ; tÞ CT ðtf ÞSCðtf Þr12 ðtf ; tÞ ð11:4-18Þ and
Optimal Control
505
1 T C ðtf ÞSCðtf Þf 1 ðtÞ lðtÞ ¼ r22 ðtf ; tÞ CT ðtf ÞSr12 ðtf ; tÞ
CT ðtf ÞSmðtf Þ f 2 ðtÞ
ð11:4-19Þ
Finally uðtÞ ¼ KðtÞxðtÞ þ qðtÞ where KðtÞ ¼ R1 ðtÞBT ðtÞPðtÞ
and
qðtÞ ¼ R1 ðtÞBT ðtÞlðtÞ
Figures 11.5 and 11.6 give an overview of the optimal servomechanism problem. Comparing these figures with Figures 11.3 and 11.4 of the optimal regulator problem we note that here an additional input vector ðtÞ is present. The vector qðtÞ is due to mðtÞ, which can be viewed as a reference vector.
Remark 11.4.2 Consider the special case where the system under control is time invariant, the weighting matrix S ¼ 0, and tf ! þ1. Then, similar results to those of Subsec. 11.3.3 can be obtained for the optimal linear servomechanism [2].
Figure 11.5
A simplified diagram of the optimal linear serovmechanism problem.
506
Chapter 11
Figure 11.6
Block diagram of the optimal linear servomechanism problem.
Example 11.4.1 Consider the system " # " #" # " # 0 1 x1 ðtÞ 0 x_ 1 ðtÞ ¼ þ uðtÞ x2 ðtÞ 0 0 1 x_ 2 ðtÞ
1 y1 ðtÞ ¼ y2 ðtÞ 0
0 1
x1 ðtÞ x2 ðtÞ
Optimal Control
507
with cost function ð
1 tf J¼ ½xðtÞ mðtÞT Q½xðtÞ mðtÞ þ uðtÞRuðtÞ dt 2 0 ð
1 tf ¼ ½x1 ðtÞ 1 ðtÞ2 þ u2 ðtÞ dt 2 0 where
Q¼
1 0
0 0
and
R¼1
Find the optimal uðtÞ as a function of xðtÞ. Solution The Riccati equation is given by P þ PA þ AT P PBR1 BT P ¼ CT QC or
p_ 11 p_ 21
0 p_ 12 þ 0 p_ 22
p11 0 þ p21 p11
0 p p 12 21 p12 p21 p22
p12 p22 p222
1 ¼ 0
0 0
Since matrix P is symmetric, namely p12 ¼ p21 , the above equation reduces to the following three nonlinear differential equations: p_ 11 p212 ¼ 1 p_ 12 þ p11 p12 p22 ¼ 0 p_ 22 þ 2p12 p222 ¼ 0 with boundary conditions Pðtf Þ ¼ CT SC ¼ 0;
since S ¼ 0
To facilitate the solution of the Riccati equation, we assume that tf ! þ1. This results in the following algebraic system of nonlinear equations: p212 ¼ 1;
p11 p12 p22 ¼ 0;
from which we obtain that p12 ¼ 1, p22 pffiffiffi 2 p1ffiffiffi P¼ 1 2
2p12 p222 ¼ 0 pffiffiffi pffiffiffi ¼ 2, and p11 ¼ 2. Consequently,
In the sequel, we determine the vector lðtÞ. We have 0 1 ffiffiffi 1 T p A BR B P ¼ 1 2 Hence, Eq. (11.4-12a) becomes
_ 1 ðtÞ 0 1 pffiffiffi 1 ðtÞ ¼ 1 ðtÞ þ
_ 2 ðtÞ 0 1 2 2 ðtÞ
508
Chapter 11
Figure 11.7
Block diagram of the closed-loop system.
When 1 ðtÞ ¼ ¼ constant, then for tf ! þ1 we further assume that
_ 1 ðtÞ ¼ _ 2 ðtÞ ¼ 0, and thus we have 2 ¼ 0:707 1 ¼ . This solution is finally substituted in Eq. (11.4-7) to obtain the optimal control signal pffiffiffi u ¼ x1 2x2 þ 2 Figure 11.7 shows the block diagram of the closed-loop system. 11.5
PROBLEMS
1. Find the optimal vector xðtÞ which minimizes the cost function ð tf ð =4 2 J¼ x1 þ 4x22 þ x_ 1 x_ 2 dt ’ðx; x; tÞ dt ¼ 0
t0
where x ¼ ½x1 x2 , and where 1 x1 ð0Þ xð0Þ ¼ ¼ 1 x2 ð0Þ T
and
x1 ð=4Þ 1 xð=4Þ ¼ ¼ 0 x2 ð=4Þ
2. Find the optimal vector xðtÞ which minimizes the cost function ð tf ð =4 2 tÞ dt ¼ J¼ x1 þ x_ 1 x_ 2 þ x_ 22 dt ’ðx; x; 0
t0
where x ¼ ½x1
x2 , and where x1 ð0Þ 1 xð0Þ ¼ ¼ and x2 ð0Þ 3=2 x1 ð=4Þ 2 xð=4Þ ¼ ¼ x2 ð=4Þ unspecified T
3. Consider the system ¼ AxðtÞ þ BuðtÞ; xðtÞ
xð0Þ ¼ x0
Optimal Control
509
yðtÞ ¼ CxðtÞ þ DuðtÞ where
0 1 A¼ ; 1 0
1 B¼ 1
1=2 1=2 C¼ ; 1=4 1=4
1 1
1 ; 1
0 0 D¼ 0 0
0 1
Find the optimal input vector uðtÞ so that the output vector yðtÞ belongs to the ellipse 1 2 4 y1 ðtÞ
þ y22 ðtÞ ¼ 1
or equivalently y1 ðtÞ ¼ 2 cos t
and
y2 ðtÞ ¼ sin t
while the following cost function is minimized: ð =2 T
u ðtÞ uðtÞ þ uT ðtÞuðtÞ dt J¼ 0
with
2
3 1 uð0Þ ¼ 4 1 5 0
2
and
3 1 uð=2Þ ¼ 4 1 5 0
4. Find the optimal input uðtÞ which minimizes the cost function ð 1 tf
½xðtÞ mðtÞT QðtÞ½xðtÞ mðtÞ þ uT ðtÞRðtÞuðtÞ dt J¼ 2 t0 for the linear time-varying system ¼ AðtÞxðtÞ þ BðtÞuðtÞ; xðtÞ xð0Þ ¼ x0 where mðtÞ is a predetermined desired state trajectory. 5. Consider the network of Figure 11.8. The capacitor’s initial voltage is x0 . At t ¼ 0, the switches S1 and S2 are closed. Find the optimal input uðtÞ which minimizes the following cost function: ð1 1 2 2 x ðtÞ þ u ðtÞ dt J¼ 5 0 Let RC ¼ 1 and consider both cases: the open- and the closed-loop system. 6. Consider the system x_ ðtÞ ¼ xðtÞ þ uðtÞ;
xð0Þ ¼ x0
Find the optimal input uðtÞ which minimizes the cost function
510
Chapter 11
Figure 11.8 ð1 1 2 2 J ¼ x ð1Þ þ x ðtÞ þ u ðtÞ dt 8 0 2
for both the open- as we well as for the closed-loop system case. 7. Consider the system x_ ðtÞ ¼ axðtÞ þ uðtÞ; and the cost function 1 1 J ¼ sx2 ðtf Þ þ 2 2
ð tf
xðt0 Þ ¼ x0
qx2 ðtÞ þ ru2 ðtÞ dt
t0
Find the optimal uðtÞ as a function of xðtÞ. 8. Consider the system x_ 1 ðtÞ 0 ¼ !2 x_ 2 ðtÞ and the cost function 1 1 J ¼ sx21 ðtf Þ þ 2 2
1 0
ð tf
x1 ðtÞ 0 þ uðtÞ; 1 x2 ðtÞ
xðt0 Þ ¼
x2 ðt0 Þ x2 ðt0 Þ
u2 ðtÞ dt
t0
Find the optimal uðtÞ as a function of xðtÞ. 9. Consider the controllable and observable system ¼ AðtÞxðtÞ þ BðtÞuðtÞ xðtÞ yðtÞ ¼ CðtÞxðtÞ The response of the closed-loop system is desired to follow the response of an ideal model described by the vector differential equation wðtÞ ¼ LðtÞwðtÞ Find the optimal control vector uðtÞ, as a function of xðtÞ, which minimizes the cost function
Optimal Control
J¼
1 2
511
ð tf
LðtÞyðtÞ þ uT ðtÞRðtÞuðtÞ dt ½ yðtÞ LðtÞyðtÞT QðtÞ½ yðtÞ
t0
where QðtÞ is symmetrical, positive semidefinite and RðtÞ is symmetrical positive definite. 10. The yaw motion of a tanker is described in state space by the equations [21] ¼ AxðtÞ þ BuðtÞ xðtÞ yðtÞ ¼ CxðtÞ where xT ðtÞ ¼ ½vðtÞ rðtÞ ðtÞ; 2 3 0:44 0:28 0 A ¼ 4 2:67 2:04 0 5; 0 1 0 In particular (see Figure 11.9),
uðtÞ ¼ ðtÞ; yT ðtÞ ¼ ½ ðtÞ vðtÞ; 2 3 0:07 0 0 4 5 B ¼ 0:53 ; C¼ 1 0 0
1 0
vðtÞ ¼ the y-component of the tanker velocity rðtÞ ¼ the tanker velocity ðtÞ ¼ the axial inclination of the tanker relative to the given frame of reference ðtÞ ¼ the rudder orientation with respect to the axial direction. Find a state feedback control law of the form uðtÞ ¼ kT xðtÞ so that the following cost function is minimized: ð1 J¼
xT ðtÞQxðtÞ þ ru2 ðtÞ dt;
0
and
Figure 11.9
r ¼ 3:
where
2
1 6 Q ¼ 40
0 2
3 0 7 15
0
1
2
512
Chapter 11
11. Consider a linear time-invariant system described by the differential equation yð2Þ ðtÞ þ 4yð1Þ ðtÞ þ 3yðtÞ ¼ uðtÞ with zero initial conditions. (a) Find the optimal solution uðtÞ ¼ f ½yðtÞ; y_ðtÞ; t of the linear serovmechanism problem which minimizes the cost function ð tf
J¼ ½y_ ðtÞ 22 þ ½yðtÞ 12 þ 3u2 ðtÞ dt t0
with tf given. Do not solve the differential equations derived in the solution procedure but put them in their simplest form and find the necessary boundary conditions for their solution. (b) Consider the same cost function as in (a), and determine the analytical expression of the optimal input uðtÞ, for tf ! 1. 12. The simplified block diagram of a position control servomechanism is shown in Figure 11.10. Let ¼ K=A and ¼ B=A. Then GðsÞ ¼ =sðs þ Þ and the differential equation of the open system is the following: yð2Þ ðtÞ þ yð1Þ ðtÞ ¼ r ðtÞ. Assume that r ðtÞ ¼ 1, and that the desired output of the closed-loop system is ðtÞ ¼ 1. Choose the following cost function: ð 1 tf
J¼ q½y ðtÞ ðtÞ2 þ rr2 ðtÞ dt; with ðtÞ ¼ 1 2 0 (a) Find the optimal r ðtÞ which minimizes the cost function. Do not solve the differential equations derived in the solution procedure but put them in their simplest form and find the necessary boundary conditions for their solution. (b) Find the optimal r ðtÞ, for tf ! 1. (Let ¼ 1, ¼ 2, q ¼ 1, and r ¼ 1). 13. For the yaw motion control system of the tanker of Problem 10, find the optimal control input uðtÞ, which minimizes the cost function ð
1 1 2 J¼ e1 ðtÞ þ 0:2e22 ðtÞ þ u2 ðtÞ dt 2 0 where e1 ðtÞ ¼ 1 ðtÞ y1 ðtÞ ¼ 1 ðtÞ x3 ðtÞ ¼ 1 ðtÞ ðtÞ e2 ðtÞ ¼ 2 ðtÞ y2 ðtÞ ¼ 2 ðtÞ x1 ðtÞ ¼ 2 ðtÞ ¼ vðtÞ
Figure 11.10
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513
and the output of the system to follow the desired course mðtÞ, where mðtÞ ¼
2 deg 1 ðtÞ ¼ 7 knots 2 ðtÞ
BIBLIOGRAPHY Books 1. 2. 3. 4. 5. 6. 7. 8.
9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.
BDO Anderson, JB Moore. Linear Optimal Control. Englewood Cliffs, New Jersey: Prentice Hall, 1971. BDO Anderson, JB Moore. Optimal Control Quadratic Methods. Englewood Cliffs, New Jersey: Prentice Hall, 1990. KJ Astrom. Introduction to Stochastic Control Theory. New York: Academic Press, 1970. M Athans, PL Falb. Optimal Control. New York: McGraw-Hill, 1966. AE Bryson, YC Ho. Applied Optimal Control. New York: Holsted Press, 1968. AE Bryson Jr, YC Ho. Applied Optimal Control. New York: John Wiley, 1975. AA Feldbaum. Optimal Control Systems. New York: Academic Press, 1965. RE Kalman. The theory of optimal control and the calculus of variations. In: R Bellman, ed. Mathematical Optimization Techniques. Berkeley: University of California Press, 1963. DE Kirk. Optimal Control Theory, An Introduction. Englewood Cliffs, New Jersey: Prentice Hall, 1970. H Kwakernaak, R Sivan. Linear Optimal Control Systems. New York: WileyInterscience, 1972. G Leitman. An Introduction to Optimal Control. New York: McGraw-Hill, 1966. RCK Lee. Optimal Estimation, Identification and Control. Cambridge, Massachusetts: Technology Press, 1964. EB Lee, L Markus. Foundations of Optimal Control Theory. New York: John Wiley, 1967. FL Lewis. Optimal Control. New York: Wiley, 1986. DG Luenberger. Optimization by Vector Space Methods. New York: John Wiley, 1969. CW Merriam. Optimization Theory and the Design of Feedback Control Systems. New York: McGraw-Hill, 1964. LS Pontryagin et al. The Mathematical Theory of Optimal Processes. New York: Interscience Publishers, 1962. HA Prime. Modern Concepts in Control Theory. New York: McGraw-Hill, 1969. AD Sage. Optimum Systems Control. Englewood Cliffs, New Jersey: Prentice Hall, 1968. AD Sage, CC White. Optimum Systems Control. 2nd ed. Englewood Cliffs, New Jersey, 1977.
Articles 21.
KJ Astrom, CG Kallstrom. Identification of ship steering dynamics. Automatica 12:9– 22, 1976.
12 Digital Control
12.1
INTRODUCTION
The aim of this chapter is to introduce the reader to the modern and very promising approach of controlling systems using a computer. Our goal is to extend, as much as possible, the material covered thus far in this book for continuous-time systems to discrete-time systems. The material of this chapter is a condensed version of most of the material presented by the author in the first five chapters of his book Digital Control Systems [13]. 12.1.1 The Basic Structure of Digital Control Systems The basic structure of a typical digital control system or computer-controlled system or discrete-time system is shown in Figure 12.1. The system (plant or process) under control is a continuous-time system (e.g., a motor, electrical power plant, robot, etc.). The ‘‘heart’’ of the controller is the digital computer. The A/D converter converts a continuous-time signal into a discrete-time signal at times specified by a clock. The D/A converter, in contrast, converts the discrete-time signal output of the computer to a continuous-time signal to be fed to the plant. The D/A converter normally includes a hold circuit (for more on A/D and D/A converters see Subsecs 12.3.1 and 12.3.2). The quantizer Q converts a discrete-time signal to binary digits. The controller may be designed to satisfy simple, as well as complex, specifications. For this reason, it may operate as a simple logic device as in programmable logic controllers (PLCs), or make dynamic and complicated processing operations on the error eðkTÞ to produce a suitable input uðtÞ to control the plant. This control input uðtÞ to the plant must be such that the behavior of the closed-loop system (i.e., the output yðtÞÞ satisfies desired specifications. The problem of realizing a digital controller is mainly one of developing a computer program. Digital controllers present significant advantages over classical analog controllers. Some of these advantages are as follows: 1.
Digital controllers have greater flexibility in modifying the controller’s features. Indeed, the controller’s features may be readily programmed. 515
516
Chapter 12
Figure 12.1
Simplified block diagram of a typical closed-loop digital control or computercontrolled system.
2.
3.
4. 5.
For classical analog controllers, any change in the characteristics of the controller is usually laborious and expensive, since it reqires changes in the structure and/or the elements of the controller. Processing of data is simple. Complex computations may be performed in a fast and convenient way. Analog controllers do not have this characteristic. Digital controllers are superior over analog controllers with regard to the following characteristics: a. Sensitivity b. Drift effects c. Internal noise d. Reliability Digital controllers are cheaper than analog controllers. Digital controllers are considerably smaller in size than analog controllers.
Nevertheless, digital controllers have certain disadvantages compared with analog controllers. The most significant disadvantage is due to the error introduced during sampling of the analog signals, as well as during the quantization of the discrete-time signals. 12.1.2
Mathematical Background
The mathematical background necessary for the study of digital control systems is the Z-transform, presented in Appendix B. The Z-transform facilitates the study and design of discrete-time control systems in an analogous way to that Laplace transform does for the continuous-time control systems. For this reason, we strongly advise that the reader becomes familiar with the material presented in Appendix B. 12.2
DESCRIPTION AND ANALYSIS OF DISCRETE-TIME SYSTEMS
The term discrete-time systems covers systems which operate directly with discretetime signals. In this case, the input, as well as the output, of the system is obviously a discrete-time signal (Figure 12.2). A well-known discrete-time system is the digital
Digital Control
Figure 12.2
517
Block diagram of a discrete-time system.
computer. In this case the signals uðkÞ and yðkÞ are number sequences (usually 0 and 1). These types of systems, as we shall see, are described by difference equations. The term sampled-data systems [1, 8] covers the usual analog (continuous-time) systems, having the following distinct characteristics: the input uðtÞ and the output yðtÞ are piecewise constant signals, i.e., they are constant over each interval between two consecutive sampling points (Figure 12.3). The piecewise constant signal uðtÞ is derived from the discrete-time signal vðkTÞ using a hold circuit (see Subsec. 12.3.2). The output sðtÞ of the system is a continuous-time function. Let yðtÞ be the output of the system having a piecewise constant form with yðtÞ ¼ sðtÞ at the sampling points. Then, the system having uðtÞ as input and yðtÞ as output, where both signals are piecewise constant in each interval, is a sampled-date system and may be described by difference equations, as shown in Subsecs 12.3.3 and 12.3.4. This means that sampled-data systems may be described and subsequently studied similarly to discrete-time systems. This fact is of particular importance since it unifies the study of hybrid systems, which consist of continuous-time and discrete-time subsystems (the computer-controlled system shown in Figure 12.1 is a hybrid system) using a common mathematical tool, namely the difference equations. For this reason, and for reasons of simplicity, sampled-data systems are usually addressed in the literature (and in this book) as discrete-time systems. It is noted that sampled-data systems are also called discretized systems. 12.2.1 Properties of Discrete-Time Systems From a mathematical point of view, discrete-time system description implies the determination of a law which assigns an output sequence yðkÞ to a given input sequence uðkÞ (Figure 12.4). The specific law connecting the input and output sequences uðkÞ and yðkÞ constitutes the mathematical model of the discrete-time system. Symbolically, this relation can be written as follows:
Figure 12.3
Block diagram of a sampled-data system.
518
Chapter 12
Figure 12.4
Block diagram of a discrete-time system.
yðkÞ ¼ Q½uðkÞ where Q is a discrete operator. Discrete-time systems have a number of properties, some of which are of special interest and are presented below. 1 Linearity A discrete-time system is linear if the following relation holds true: Q½c1 u1 ðkÞ þ c2 u2 ðkÞ ¼ c1 Q½u1 ðkÞ þ c2 Q½u2 ðkÞ ¼ c1 y1 ðkÞ þ c2 y2 ðkÞ
ð12:2-1Þ
for every c1 , c2 , u1 ðkÞ, and u2 ðkÞ, where c1 , c2 are constants and y1 ðkÞ ¼ Q½u1 ðkÞ is the output of the system with input u1 ðkÞ and y2 ðkÞ ¼ Q½u2 ðkÞ is the output of the system with input u2 ðkÞ: 2 Time-Invariant System A discrete-time system is time-invariant if the following holds true: Quðk k0 Þ ¼ yðk k0 Þ
ð12:2-2Þ
for every k0 . Equation (12.2-2) shows that when the input to the system is shifted by k0 units, the output of the system is also shifted by k0 units. 3 Causality A discrete-time system is called causal if the output yðkÞ ¼ 0 for k < k0 , when the input uðkÞ ¼ 0 for k < k0 . A discrete-time signal is called causal if it is zero for k < k0 . Hence, a system is causal if every causal excitation produces a causal response.
12.2.2
Description of Linear Time-Invariant Discrete-Time Systems
A linear time-invariant causal discrete-time system involves the following elements: summation units, amplification units, and delay units. The block diagram of all three elements is shown in Figure 12.5. The delay unit is designated as z1 , meaning that the output is identical to the input delayed by a time unit. When these three elements are suitably interconnected, then one has a discrete-time system, as, for example, the first-order discrete-time system shown in Figure 12.6. Adding the three signals at the summation point , we arrive at the equation yðkÞ þ a1 yðk 1Þ ¼ b0 uðkÞ þ b1 uðk 1Þ
ð12:1-3aÞ
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Figure 12.5
519
(a) Summation, (b) amplification, and (c) delay units.
Similarly, for the second-order discrete-time system shown in Figure 12.7, we obtain the equation yðkÞ þ a1 yðk 1Þ þ a2 yðk 2Þ ¼ b0 uðkÞ þ b1 uðk 1Þ þ b2 uðk 2Þ
ð12:2-3bÞ
Obviously, Eqs (12.2-3a and b) are mathematical models describing the discrete-time systems shown in Figures 12.6 and 12.7, respectively. Equations (12.2-3a and b) are examples of difference equations. There are many ways to describe discrete-time systems, as is also the case for continuous-time systems. The most popular ones are the following: the difference equations, as in Eqs (12.2-3a and b); the transfer function; the impulse response or weight function; and the state-space equations. In presenting these four methods, certain similarities and dissimilarities between continuous-time and discrete-time systems will be revealed. There are
Figure 12.6
Block diagram of a first-order discrete-time system.
520
Chapter 12
Figure 12.7
Block diagram of a second-order discrete-time system.
three basic differences, going from continuous-time to discrete-time systems: differential equations are now difference equations; the Laplace transform gives way to the Z-transform (see Appendix B); and the integration procedure is replaced by summation. 1 Difference Equations The general form of a difference equation is as follows: yðkÞ þ a1 yðk 1Þ þ þ an yðk nÞ ¼ b0 uðkÞ þ b1 uðk 1Þ þ þ bm uðk mÞ ð12:2-4Þ with initial conditions yð1Þ; yð2Þ; . . . ; yðnÞ. The solution of Eq. (12.2-4) may be found either in the time domain (using methods similar to those for solving a differential equation in the time domain) or in the complex frequency or z-domain using the Z-transform. 2 Transfer Function The transfer function of a discrete-time system is denoted by HðzÞ and is defined as the ratio of the Z-transform of the output yðkÞ divided by the Z-transform of the input uðkÞ, under the condition that uðkÞ ¼ yðkÞ ¼ 0, for all negative values of k. That is HðzÞ ¼
Z½yðkÞ YðzÞ ¼ ; Z½uðkÞ UðzÞ
where
uðkÞ ¼ yðkÞ ¼ 0
for
k<0 ð12:2-5Þ
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521
The transfer function of a system described by the difference equation (12.2-4), with yðkÞ ¼ uðkÞ ¼ 0, for k < 0, is determined as follows: multiply both sides of Eq. (12.24) by the term zk and add for k ¼ 0; 1; 2; . . . ; 1, to yield: 1 X
yðkÞzk þ a1
k¼0
þ an þ bm
1 X k¼0 1 X
yðk 1Þzk þ a2 yðk nÞzk ¼ b0
k¼0 1 X
1 X k¼0 1 X
yðk 2Þzk þ uðkÞzk þ b1
k¼0
1 X
uðk 1Þzk þ
k¼0
uðk mÞzk
k¼0
Using the Z-transform time-shifting property given by Eq. (B.3-12a) in Appendix B and the assumption that uðkÞ and yðkÞ are zero for negative values of k, the above equation can be simplified as follows: YðzÞ þ a1 z1 YðzÞ þ þ an zn YðzÞ ¼ b0 uðzÞ þ b1 z1 UðzÞ þ þ bm zm UðzÞ where use was made of the definition of the Z-transform (Eq. (B.3-1)) in Appendix B. Hence, using definition (12.2-5), we arrive at the following rational polynomial form for HðzÞ: HðzÞ ¼
YðzÞ b0 þ b1 z1 þ þ bm zm ¼ UðzÞ 1 þ a1 z1 þ þ an zn
ð12:2-6Þ
3 Impulse Response or Weight Function The impulse response (or weight function) of a system is denoted by hðkÞ and is defined as the output of a system when its input is the unit impulse sequence ðkÞ (see Figure B.1 in Appendix B), under the constraint that the initial conditions yð1Þ; yð2Þ; . . . ; yðnÞ of the system are zero. The block diagram definition of the impulse response is shown in Figure 12.8. The transfer function HðzÞ and the weight function hðkÞ are related by the following equation: HðzÞ ¼ Z½hðkÞ
ð12:2-7Þ
where Z½f ðkÞ indicates the Z-transform of f ðkÞ defined by Eq. (B.3-1) in Appendix B. 4 State-Space Equations State-space equations or simply state equations is a set of first-order difference equations describing high-order systems and have the form
Figure 12.8
Block diagram definition of the impulse response.
522
Chapter 12
xðk þ 1Þ ¼ AxðkÞ þ BuðkÞ yðkÞ ¼ CxðkÞ þ DuðkÞ
ð12:2-8aÞ ð12:2-8bÞ
where uðkÞ 2 Rm , xðkÞ 2 Rn , and yðkÞ 2 Rp , are the input, state, and output vectors, respectively, and A, B, C, and D are constant matrices of appropriate dimensions. Let YðzÞ ¼ Z½yðkÞ and UðzÞ ¼ Z½uðkÞ. Then, the transfer function matrix HðzÞ of Eqs (12.2-8) is given by HðzÞ ¼ CðzIn A1 B þ D The impulse response matrix HðkÞ of Eqs (12.2-8) is given by D; for k¼0 1 HðkÞ ¼ Z ½HðzÞ ¼ CAk1 B; for k>0 12.2.3
ð12:2-9Þ
ð12:2-10Þ
Analysis of Linear Time-Invariant Discrete-Time Systems
The problem of the analysis of linear time-invariant discrete-time systems will be treated using four different methods, where each method corresponds to one of the four description models presented in Subsec. 12.2.2. 1 Analysis Based on the Difference Equation We present the following introductory example. Example 12.2.1 A discrete-time system is described by the difference equation yðkÞ ¼ uðkÞ þ ayðk 1Þ with the initial condition yð1Þ. Solve the difference equation, i.e., determine yðkÞ. Solution The difference equation may be solved to determine yðkÞ, using the Z-transform, as follows. Take the Z-transform of both sides of the equation to yield Z½ yðkÞ ¼ Z½uðkÞ þ aZ½ yðk 1Þ or YðzÞ ¼ UðzÞ þ a½z1 YðzÞ þ yð1Þ and thus YðzÞ ¼
z½UðzÞ þ ayð1Þ ayð1Þz UðzÞz ¼ þ za za za
Suppose that the excitation uðkÞ is the unit step sequence ðkÞ (see Figure B.2 in Appendix B). In this case z UðzÞ ¼ Z½ ðkÞ ¼ z1 Then, the output YðzÞ becomes YðzÞ ¼
ayð1Þz z2 ayð1Þz 1 h z i h a ih z i þ þ ¼ za za 1a z1 1a za ðz aÞðz 1Þ
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523
Take the inverse Z-transform (see Appendix C) to yield yðkÞ ¼ akþ1 yð1Þ þ
1 1 akþ1 1a 1a
The expression for the output yðkÞ clearly converges for jaj < 1. The initial condition yð1Þ contributes only during the transient period. The output yðkÞ, in the steady state, takes on the form yss ðkÞ ¼
1 1a
where yss ðkÞ denotes the steady-state value of yðkÞ. Figure 12.9 shows yðkÞ when the initial condition yð1Þ ¼ 0, the input uðkÞ ¼ ðkÞ, and jaj < 1. 2 Analysis Based on the Transfer Function The input UðzÞ, the output YðzÞ, and the transfer function HðzÞ are related by the equation YðzÞ ¼ HðzÞUðzÞ Hence yðkÞ ¼ Z 1 ½YðzÞ ¼ Z 1 ½HðzÞUðzÞ 3 Analysis Based on the Impulse Response The input uðkÞ, the output yðkÞ, and the impulse response hðkÞ are related via the following convolution relation: yðkÞ ¼ uðkÞ hðkÞ ¼
1 X
uðiÞhðk iÞ
ð12:2-11Þ
i¼1
If the system is causal, i.e., if hðkÞ ¼ 0 for k < 0, then relation (12.2-11) becomes yðkÞ ¼
1 X i¼0
Figure 12.9 jaj < 1.
uðiÞhðk iÞ ¼
1 X
uðk iÞhðiÞ
ð12:2-12Þ
i¼0
Response of system of Example 12.2.1 when yð1Þ ¼ 0, uðkÞ ¼ ðkÞ, and
524
Chapter 12
If both the system and the input signal are causal, i.e., if hðkÞ ¼ 0 and uðkÞ ¼ 0 for k < 0, then Eq. (12.2-12) becomes yðkÞ ¼
k X
k X
hðiÞuðk iÞ ¼
i¼0
hðk iÞuðiÞ
ð12:2-13Þ
i¼0
The values yðoÞ; yð1Þ; . . . of the output yðkÞ can be calculated from Eq. (12.2-13) as follows: yð0Þ ¼ hð0Þuð0Þ yð1Þ ¼ hð0Þuð1Þ þ hð1Þuð0Þ yð2Þ ¼ hð0Þuð2Þ þ hð1Þuð1Þ þ hð2Þuð0Þ .. . yðkÞ ¼ hð0ÞuðkÞ þ hð1Þuðk 1Þ þ þ hðkÞuð0Þ or more compactly as y ¼ Hu ¼ Uh where
ð12:2-14Þ 3 uð0Þ 6 uð1Þ 7 7 6 u ¼ 6 . 7; 4 .. 5
3 yð0Þ 6 yð1Þ 7 7 6 y ¼ 6 . 7; 4 .. 5
2
2
yðkÞ 2 hð0Þ 0 6 hð1Þ hð0Þ 6 H¼6 .. . 6 . 4 . . hðkÞ hðk 1Þ 2 uð0Þ 0 6 uð1Þ uð0Þ 6 U¼6 .. 6 .. 4 . . uðkÞ uðk 1Þ
uðkÞ
2
3 hð0Þ 6 hð1Þ 7 7 6 h¼6 . 7 4 .. 5
and
hðkÞ
3
0 0 7 7 .. 7 7; . 5 hð0Þ 3 0 0 7 7 .. 7 7 . 5
and
uð0Þ
Remark 12.2.1 Equation (12.2-14) can be used for the determination of the impulse response hðkÞ based on the input uðkÞ and the output yðkÞ. Indeed, from Eq. (12.2-14), we have that h ¼ U1 y;
if
uð0Þ 6¼ 0
ð12:2-15Þ
The above procedure is called deconvolution (since it is the reverse of convolution) and constitutes a simple identification method for a discrete-time system. For more on the issue of identification see Chap. 13. 4 Analysis Based on the State Equations Consider the state equations (12.2-8). From Eq. (12.2-8a),
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525
xðk þ 1Þ ¼ AxðkÞ þ BuðkÞ we have that For k ¼ 0 : xð1Þ ¼ Axð0Þ þ Buð0Þ For k ¼ 1 : xð2Þ ¼ Axð1Þ þ Buð1Þ ¼ A½Axð0Þ þ Buð0Þ þ Buð1Þ ¼ A2 xð0Þ þ ABuð0Þ þ Buð1Þ For k ¼ 2 : xð3Þ ¼ Axð2Þ þ Buð2Þ ¼ A½A2 xð0Þ þ ABuð0Þ þ Buð1Þ þ Buð2Þ ¼ A3 xð0Þ þ A2 Buð0Þ þ ABuð1Þ þ Buð2Þ If we continue this procedure for k ¼ 3; 4; 5; . . . we arrive at the following general expression for xðkÞ: xðkÞ ¼ Ak xð0Þ þ Ak1 Buð0Þ þ Ak2 Buð1Þ þ þ ABuðk 2Þ þ Buðk 1Þ or more compactly xðkÞ ¼ Ak xð0Þ þ
k1 X
ð12:2-16Þ
Aki1 BuðiÞ
i¼0
According to Eq. (12.2-8b), the output vector yðkÞ is yðkÞ ¼ CxðkÞ þ DuðkÞ or yðkÞ ¼ CAk xð0Þ þ C
k1 X
Aki1 BuðiÞ þ DuðkÞ
ð12:2-17Þ
i¼0
where use was made of Eq. (12.2-16) The matrix Ak is called the fundamental or transition matrix of system (12.2-8) and is denoted as follows: ð12:2-18Þ
rðkÞ ¼ Ak
The matrix rðkÞ is analogous to the matrix rðtÞ of the continuous-time systems (see Table 12.1).
Table 12.1 Comparison of the Description Methods Between Continuous-Time and Discrete-Time Systems Description method
Continuous-time system
State-space equations
¼ FxðtÞ þ GuðtÞ xðtÞ yðtÞ ¼ CxðtÞ þ DuðtÞ
xðk þ 1Þ ¼ FxðkÞ þ GuðkÞ yðkÞ ¼ CxðkÞ þ DuðkÞ
Transition matrix
ðtÞ ¼ eFt
rðkÞ ¼ Ak 1
Discrete-time system
rðzÞ ¼ zðzI AÞ1
L=Z-transform of transition matrix
rðsÞ ¼ ðsI FÞ
Transfer function matrix
HðsÞ ¼ CrðsÞG þ D
HðzÞ ¼ z1 CrðzÞB þ D
Impulse response matrix
HðtÞ ¼ C ðtÞG þ DðtÞ
HðkÞ ¼ Crðk 1ÞG þ D for k > 0 HðkÞ ¼ D for k ¼ 0
526
Chapter 12
The state vector xðkÞ may also be calculated from Eq. (12.2-8a) using the Ztransform as follows. Take the Z-transform of both sides of the equation to yield zXðzÞ zxð0Þ ¼ AXðzÞ þ BUðzÞ or XðzÞ ¼ z½zI A1 xð0Þ þ ½zI A1 BUðzÞ Taking the inverse Z-transform, we have xðkÞ ¼ Ak xð0Þ þ Ak1 BuðkÞ ¼ Ak xð0Þ þ
k1 X
ð12:2-19Þ
Aki1 BuðiÞ
i¼0
Equation (12.2-19) is in agreement with Eq. (12.2-16), as expected. It is evident that the state transition matrix can also be expressed as rðkÞ ¼ Ak ¼ Z 1 ½zðzI AÞ1
ð12:2-20Þ
A comparison between the description methods used for continuous-time and discrete-time systems is shown in Table 12.1. Example 12.2.2 Find the transition matrix, the state and the output vectors of a discrete-time system with zero initial condition, with uðkÞ ¼ ðkÞ and 0 1 0 1 A¼ ; b¼ ; and c¼ 1 1 2 3 Solution We have rðzÞ ¼ ZðrðkÞ ¼ zðzI AÞ Hence
2
zðz 3Þ 6Z z2 3z þ 2 6 rðkÞ ¼ 6 6 2z 4 Z1 z2 3z þ 2 1
1
1 zðz 3Þ ¼ 2 2z z 3z þ 2
z z2
3 z Z z2 3z þ 2 7 7 " #7 2 7 z 5 Z 1 2 z 3z þ 2 1
Using the Z-transform pairs given in Appendix C, one obtains 2 2k 2k 1 rðkÞ ¼ 2ð1 2k Þ 2kþ1 1 These results may be checked as follows. Since rðkÞ ¼ Ak , it follows that rð0Þ ¼ I and rð1Þ ¼ A. Moreover, from the initial value theorem it follows that limz!1 rðzÞ ¼ rð0Þ. Indeed 1 0 rð0Þ ¼ lim rðzÞ ¼ ¼1 0 1 z!1
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Since the initial conditions are zero, the state vector may be calculated as follows: rðzÞ 1 z 1 BuðzÞ ¼ XðzÞ ¼ ðzI AÞ BuðzÞ ¼ 2 2 z z ðz 1Þ ðz 2Þ Using the Z-transform pairs given in the Appendix C, one obtains k 2 k1 xðkÞ ¼ Z1 ½XðzÞ ¼ 2ð2k 1Þ k From this expression, it follows that xð0Þ ¼ 0. Finally, the output of the system is given by yðkÞ ¼ cT xðkÞ ¼ 2k k 1 þ ð2Þ ð2k Þ 2 k ¼ ð3Þ ð2k Þ 2k 3 Remark 12.2.2 When the initial conditions hold for k ¼ k0 , the above results take on the following general forms: rðk; k0 Þ ¼ Akk0 xðkÞ ¼ rðk k0 Þxðk0 Þ þ
ð12:2-21aÞ k1 X
rðk i 1ÞBuðiÞ
ð12:2-21bÞ
i¼k0
yðkÞ ¼ Crðk k0 Þxðk0 Þ þ
k1 X
Crðk i 1ÞBuðiÞ þ DuðkÞ
ð12:2-21cÞ
i¼k0
12.3
DESCRIPTION AND ANALYSIS OF SAMPLED-DATA SYSTEMS
12.3.1 Introduction to D/A and A/D Converters As we have already noted in Subsec. 12.1.1, in modern control systems a continuoustime system is usually controlled using a digital computer (Figure 12.1). As a result, the closed-loop system involves continuous-time, as well as discrete-time, subsystems. To have a common base for the study of the closed-loop system, it is logical to use the same mathematical model for both continuous-time and discrete-time systems. The mathematical model uses difference equations. This approach unifies the study of closed-loop systems. Furthermore, it facilitates the study of closed-loop systems, since well-known methods and results, such as transfer function, stability criteria, controller design techniques, etc., may be extended to cover the case of discrete-time systems. A practical problem which we come across in such systems is that the output of a discrete-time system, which is a discrete-time signal, may be the input to a continuous-time system (in which case, of course, the input ought to be a continuoustime signal). And vice versa, the output of a continuous-time system, which is a continuous-time signal, could be the input to a discrete-time system (which, of course, ought to be a discrete-time signal). This problem is dealt with using special devices called converters. There are two types of converters: D/A converters, which convert the discrete-time signals to analog or continuous-time signals (Figure
528
Chapter 12
12.10a), and A/D converters, which convert analog or continuous-time signals to discrete-time signals (Figure 12.10b). The constant T is the sampling time period. It is noted that before the discrete-time signal yðkTÞ of the A/D converter is fed into a digital computer, it is first converted to a digital signal with the help of a device called a quantizer. The digital signal is a sequence of 0 and 1 digits (see also Figure 12.1). 12.3.2 Hold Circuits A D/A converter is actually a hold circuit whose output yðtÞ is a piecewise constant function. Specifically, the operation of the hold circuit (Figure 12.10a) is described by the following equations: yðtÞ ¼ uðkTÞ;
for
kT t < ðk þ 1ÞT
ð12:3-1aÞ
or yðkT þ Þ ¼ uðkT Þ;
for
0
ð12:3-1bÞ
We will show that the operation of a hold circuit, described by Eqs (12.3-1a and b), may be equivalently described (from a mathematical point of view) by the idealized
Figure 12.10
The operation of (a) D/A and (b) A/D converters.
Digital Control
Figure 12.11
529
Hold circuit block diagrams: (a) hold circuit; (b) idealized hold circuit.
hold circuit shown in Figure 12.11b. Here it is assumed that the signal uðtÞ goes through an ideal sampler T , whose output u ðtÞ is given by the relation 1 X u ðtÞ ¼ uðkTÞðt kTÞ ð12:-2Þ k¼0
Clearly, u ðtÞ is a sequence of impulse functions. Applying the Laplace transform on u ðtÞ, we obtain U ðsÞ ¼
1 X
uðkTÞeskT
ð12:3-3Þ
k¼0
Therefore, the output YðsÞ is 1 1 esT X YðsÞ ¼ uðkTÞeskT s k¼0
ð12:3-4Þ
We would have arrived at the same result as in Eq. (12.3-4) if we had calculated YðsÞ of the hold circuit shown in Figure 12.11a. Indeed, since the impulse response hðtÞ of the hold circuit is a gate function (see Figure B.3 in Appendix B), i.e., hðtÞ ¼ ðtÞ ðt TÞ, the output yðtÞ should be the convolution of uðkTÞ and hðtÞ, i.e., 1 X yðtÞ ¼ uðkTÞ hðtÞ ¼ uðkT Þ½ ðt kTÞ ðt kT TÞ ð12:3-5Þ k¼0
Taking the Laplace transform of Eq. (12.3-5), we have " # 1 1 X eskT esðkþ1ÞT 1 esT X uðkTÞ uðkTÞskT YðsÞ ¼ ¼ s s k¼0 k¼0
ð12:3-6Þ
Equations (12.3-6) and (12.3-4) are identical. Therefore, both configurations in Figures 12.11a and b are equivalent with regard to the output yðtÞ. It is clear that the device shown in Figure 12.11b cannot be realized in practice and it is used only because it facilitates the mathematical description of the hold circuit. Figure 12.12 shows a typical output of the present hold circuit (known as a zero-order hold circuit).
530
Chapter 12
Figure 12.12
12.3.3
Typical output of a zero-order hold circuit.
Conversion of GðsÞ to GðzÞ
To convert a continuous-time transfer function GðsÞ to the transfer function GðzÞ of the respective discretized system, various techniques have been proposed. In what follows, we briefly present some of the most popular techniques. 1 The Backward Difference Method For simplicity, consider the case of a first-order system described by the transfer function GðsÞ ¼
YðsÞ a ¼ UðsÞ s þ a
ð12:3-7Þ
The system’s differential equation is yð1Þ ¼ ay þ au
ð12:3-8Þ
Integrating both sides of the differential equation from 0 to t, one obtains ðt ðt ðt dy dt ¼ a ydt þ a udt 0 dt 0 0 Suppose that we want to determine the values of the output yðtÞ at the sampling instants, i.e., at the points where t ¼ kT. Then, the integral equation above becomes ð kT ð kT ð kT dy dt ¼ a ydt þ a udt 0 dt 0 0 Hence, we have ð kT yðkTÞ yð0Þ ¼ a
ð kT ydt þ a
0
ð12:3-9Þ
udt 0
Substituting kT by ðk 1ÞT in this equation, one obtains ð ðk1ÞT ð ðk1ÞT y½ðk 1ÞT yð0Þ ¼ a ydt þ a udt 0
0
Substracting Eq. (12.3-10) from Eq. (12.3-9), we further obtain
ð12:3-10Þ
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yðkTÞ y½ðk 1ÞT ¼ a
ð kT
ydt þ a
ð kT
ðk1ÞT
udt
ð12:3-11Þ
ðk1ÞT
Both terms on the right-hand side of Eq. (12.3-11) may be calculated approximately in various ways. If the approximation is done as shown in Figure 12.13a, i.e., by applying the backward difference method, then Eq. (12.3-11) takes the form yðkTÞ ¼ y½ðk 1ÞT aT½yðkTÞ uðkTÞ
ð12:3-12Þ
Obviously, Eq. (12.3-12) is the equivalent difference equation of the differential equation (12.3-8). To find GðzÞ, we only need to take the Z-transform of Eq. (12.3-12) to yield GðzÞ ¼
YðzÞ a # ¼" UðzÞ 1 z1 T
ð12:3-13Þ þa
Extending the results of the above example to the general case, we arrive at the following procedure for discretizing GðsÞ using the backward difference method:
Figure 12.13 Area approximation using (a) the backward difference method, (b) the forward difference method, and (c) the Tustin or the trapezoidal method.
532
Chapter 12
GðzÞ ¼ GðsÞ
ð12:3-14Þ s¼ð1z1 Þ=T
2 The Forward Difference Method In this case, the approximation of the two terms of the right-hand side of Eq. (12.311) is done, as shown in Figure 12.13b. Working in the same way as in the previous case, we arrive at the following result for discretizing GðsÞ using the forward difference method: GðzÞ ¼ GðsÞ ð12:3-15Þ s¼ 1z1 =Tz1
3 The Bilinear Transformation Method or Trapezoidal Method or Tustin Transformation Method The Tustin transformation is based on the approximation of the two terms on the right-hand side of Eq. (12.3-11) using the trapezoidal rule, as shown in Figure 12.13a. This leads to the following result for discretizing GðsÞ: GðzÞ ¼ GðsÞ ð12:3-16Þ s¼2=T ½ð1z1 Þ=ð1þz1 Þ 4 The Invariant Impulse Response Method In this case, both GðsÞ and GðzÞ present a common characteristic in that their respective impulse functions gðtÞ and gðkTÞ are equal for t ¼ kT. This is achieved when GðzÞ ¼ Z½gðkTÞ;
where
gðkTÞ ¼ ½L1 GðsÞt¼kT
ð12:3-17Þ
5 The Invariant Step Response Method In this case, both GðsÞ and GðzÞ present the common characteristic that their step responses, i.e., the response yðtÞ produced by the excitation uðtÞ ¼ 1 and the response yðkTÞ produced by the excitation uðkTÞ ¼ 1, are equal for t ¼ kT. This is achieved when 1 1 1 1 Z ¼ L GðsÞ GðzÞ ð12:3-18Þ s t¼kT 1 z1 where, obviously, the left-hand side of Eq. (12.3-18) is equal to yðkTÞ, whereas the right-hand side is equal to yðtÞ at t ¼ kT. Applying the Z-transform on Eq. (12.3-18), we obtain GðsÞ 1 eTs ¼Z GðsÞ ¼ Z½Gh ðsÞGðsÞ ð12:3-19Þ GðzÞ ¼ 1 z1 Z s s where Gh ðsÞ is the transfer function of the zero-order hold circuit presented in Sec. 12.3.2. 6 Pole-Zero Matching Method Consider the transfer function
Digital Control
GðsÞ ¼ Ks
533
ðs þ 1 Þðs þ 2 Þ ðs þ m Þ ; ðs þ 1 Þðs þ 2 Þ ðs þ n Þ
mn
ð12:3-20Þ
Then, the pole-zero matching method assumes that GðzÞ has the general form GðzÞ ¼ Kz
ðz þ 1Þnm ðz þ z1 Þðz þ z2 Þ ðz þ zm Þ ðz þ p1 Þðz þ p2 Þ ðz þ pn Þ
ð12:3-21Þ
where the zi ’s and pi ’s are ‘‘matched’’ to the respective i ’s and i ’s according to the following relations zi ¼ e i T
and
pi ¼ ei T
ð12:3-22Þ
The n m multiple zeros ðz þ 1Þ which appear in GðzÞ represent the order difference between the numerator’s polynomial and the denominator’s polynomial in Eq. (12.3-20). The constant Kz is calculated so as to satisfy particular requirements. For example, when we are interested in the behavior of a system at low frequencies (and this is the usual case in control systems), Kz is chosen such that GðsÞ and GðzÞ are equal for s ¼ 0 and z ¼ 1, respectively, i.e., the following relation holds: ð1 þ z1 Þð1 þ z2 Þ ð1 þ zm Þ
m ¼ GðsÞ ¼ Ks 1 2 GðzÞ ¼ Kz 2nm ð1 þ p Þð1 þ p Þ ð1 þ p Þ 1 2 n 1 2 n z¼1 s¼0 ð12:3-23Þ nm
Using Eq. (12.3-23), one can easily determine Kz . Note that when a second-order term appears in GðsÞ, then the pole-zero matching method yields the following ‘‘matching’’: ðs þ aÞ2 þ b2 ) z2 2ðeaT cos bTÞz þ e2aT Example 12.3.1 For the system with transfer function GðsÞ ¼ a=ðs þ aÞ, find all above six equivalent descriptions of GðzÞ. Solution After several simple algebraic calculations, all six descriptions of GðzÞ are found and summarized in Table 12.2. Example 12.3.2 Consider a second-order continuous-time system having the following transfer function: HðsÞ ¼
b sðs þ aÞ
Find HðzÞ using the invariant impulse response method. Solution We have
b 1 1 b hðtÞ ¼ L1 HðsÞ ¼ L1 ¼ 1 eat a s ðs þ aÞ a
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Chapter 12
Table 12.2 Equivalent Discrete-Time Transfer Functions GðzÞ of the Continuous-Time Transfer Function GðsÞ ¼ a=ðs þ aÞ. Conversion of GðsÞ to GðzÞ
Method
Equivalent discrete-time transfer function
Backward difference method
1 z1 s¼ T
a # GðzÞ ¼ " 1 z1 þa T
Forward difference method
1 z1 s¼ Tz1
a # GðzÞ ¼ " 1 z1 þa Tz1 GðzÞ ¼ "
Tustin transformation
" # 2 1 z1 s¼ T 1 þ z1
Invariant impulse response method
GðzÞ ¼ Z½gðtÞ where gðtÞ ¼ Z1 GðsÞ
GðzÞ ¼
Invariant step response method
GðzÞ ¼ Z
Pole-zero matching method
GðzÞ ¼ Kz
1 eTs GðsÞ s
zþ1 z þ p1
a
2 1 z1 T 1 þ z1
!# þa
a 1
eAt z1
ð1 eaT Þz1 1 eaT z1 # " 1 eaT 1 þ z1 GðzÞ ¼ 2 1 eaT z1
GðzÞ ¼
Hence
hðkTÞ ¼ hðtÞ
¼ t¼kT
b 1 eakT a
The Z-transform of hðkTÞ is b b 1 bX akT k a a 1e z ¼ HðzÞ ¼ Z½hðkTÞ ¼ a k¼0 1 z1 1 eaT z1 or
bz1 1 eaT HðzÞ ¼ a 1 z1 1 eaT z1
Example 12.3.3 Consider a second-order continuous-time system having the following transfer function: HðsÞ ¼
2 ðs þ 1Þðs þ 2Þ
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535
Find HðzÞ using the invariant step response method. Solution We have
HðsÞ 1 eTs 1 HðzÞ ¼ Z½Gh ðsÞHðsÞ ¼ Z HðsÞ ¼ 1 z Z s s
Using the relation HðsÞ 2 1 2 1 ¼ ¼ þ s sðs þ 1Þðs þ 2Þ s s þ 1 s þ 2 and the Z-transform tables of Appendix C, we obtain HðsÞ 1 2 1 Z ¼ þ 1 T 1 2T s 1z 1e z 1 e z1 Hence
HðsÞ 1 2 1 HðzÞ ¼ 1 z1 Z ¼ 1 z1 þ s 1 z1 1 eT z1 1 e2T z1
12.3.4 Conversion of Differential State Equations to Difference State Equations Consider the continuous-time multi-input–multi-output (MIMO) open-loop system shown in Figure 12.14. Let this system be described in state space by the equations ¼ FxðtÞ þ GmðtÞ xðtÞ ð12:3-24aÞ yðtÞ ¼ CxðtÞ þ DmðtÞ
ð12:3-24bÞ
We will show that Eqs (12.3-24a and b) may be approximately written in the form of difference equations. To this end, consider the piecewise constant excitation vector mðtÞ described by mðtÞ ¼ uðkTÞ;
for
kT t < ðk þ 1ÞT
Then, solving Eq. (12.3-24a) for xðtÞ, we have ðt xðtÞ ¼ eFt xð0Þ þ eFðtÞ GmðÞd
ð12:3-25Þ
ð12:3-26Þ
0
According to Eq. (12.-3-25), mð0Þ ¼ uð0Þ for 0 t < T and hence Eq. (12.3-26) becomes ðt xðtÞ ¼ eFt xð0Þ þ eFðtÞ Guð0Þd; 0t
The state vector xðtÞ, for t ¼ T, will be
Figure 12.14
Open-loop system with a sampler and a hold circuit.
536
Chapter 12
ðT xðTÞ ¼ e
eFðTÞ Guð0Þd
ð12:3-28Þ
AðTÞ ¼ eFT ðT ðT BðTÞ ¼ eFðTÞ Gd ¼ eF Gd;
ð12:3-29aÞ
FT
xð0Þ þ 0
Define
0
¼T
ð12:3-29bÞ
0
Then, for t ¼ T, Eq. (12.3-27) can be simplified as follows: xðTÞ ¼ AðTÞxð0Þ þ BðTÞuð0Þ
ð12:3-30Þ
Repeating the above procedure for T t < 2T, 2T t < 3T, etc., we arrive at the following general formula: x½ðk þ 1ÞT ¼ AðTÞxðkTÞ þ BðTÞuðkT Þ The output equation (12.3-24b) may therefore be written as yðkTÞ ¼ CxðkT Þ þ DuðkTÞ Hence, the state differential equations (12.3-24) can be written as a system of difference equations, as follows: x½ðk þ 1ÞT ¼ AðTÞxðkTÞ þ BðTÞuðkT Þ
ð12:3-31aÞ
yðkTÞ ¼ CxðkT Þ þ DuðkTÞ
ð12:3-31bÞ
The state equations (12.3-24) and (12.3-31) are equivalent only for the time instants t ¼ kT under the constraint that the input vectors mðtÞ and uðtÞ satisfy the condition (12.3-25). Remark 12.3.1 The transfer function matrix of the continuous-time system (12.3-24) is HðsÞ ¼ CðsI FÞ1 G þ D and the transfer function matrix of the equivalent discretetime system (12.3-31) is GðzÞ ¼ C½zI AðTÞ1 BðTÞ þ D. These two matrices are related as follows: 1 esT GðzÞ ¼ Z½Gh ðsÞHðsÞ ¼ Z HðsÞ ð12:3-32Þ s This means that the matrix GðzÞ is equivalent to the matrix HðsÞ in the sense of the invariant step response (see Eq. (12.3-19)). Remark 12.3.2 Going from the continuous-time state-space description (12.3-24) to the discrete-time (sampled-data) state-space description (12.3-31), we need to determine the matrices AðTÞ and BðTÞ using the definition (12.3-29). For the determination of AðTÞ we note that it can be easily carried out using the following relation:
ð12:3-33aÞ AðTÞ ¼ eFt ¼ L1 ½sI F1 t¼T t¼T
Equation (12.3-33a) facilitates the determination of BðTÞ, since, according to the definition (12.3-29b) of BðTÞ, we have that
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537
ðT BðTÞ ¼
1
1
L ½sI F
ðT
0
t¼
Gd ¼
ð12:3-33bÞ
AðÞGd 0
Example 12.3.4 Consider the continuous-time system ¼ FxðtÞ þ gmðtÞ xðtÞ yðtÞ ¼ cT xðtÞ where
0 ; 0
1 F¼ 1
2 g¼ ; 1
0 c¼ 1
Find the equivalent discrete-time (sampled-data) system, i.e., find the matrix AðTÞ and the vector bðTÞ. Solution We have " sI F ¼
sþ1
0
1
s "
1
1
L ½sI F
¼
3 1 07 6 sþ1 6 7 ¼6 7; 4 1 15 sðs þ 1Þ s 2
# ;
ðsI FÞ1
et
0
1 et
1
#
Therefore, from Eq. (12.3-33a), we obtain T 1
e 1 FT AðTÞ ¼ L ½sI F t¼T ¼ e ¼ 1 eT
0 1
and
Moreover, from Eq. (12.3-33b), we obtain # # ð T " ðT " ðT e 0 2 2e F e gd ¼ d ¼ d bðTÞ ¼ 1 e 1 1 3 2e 0 0 0 " # 2ð1 eT Þ ¼ 3T 2ð1 eT Þ Example 12.3.5 Consider a harmonic oscillator with transfer function GðsÞ ¼
YðsÞ !2 ¼ 2 UðsÞ s þ !2
A state-space description of the oscillator is of the form x ¼ Fx þ gu, y ¼ cT x, where y 0 ! 0 1 x1 ¼ F¼ ; g¼ ; c¼ x¼ 1 ð1Þ ; x2 ! y ! 0 ! 0
538
Chapter 12
Find the equivalent discrete-time (sampled-data) system of the form (12.3-31), i.e., find the matrix AðTÞ and the vector bðTÞ. Solution We have 2
1
ðsI FÞ
s 6 s þ !2 ¼ 4 ! s2 þ ! 2 2
! 3 s þ !2 7 s 5 2 s þ !2
"
2
and
e ¼ Ft
cos !t
sin !t
sin !t
cos !t
#
Therefore
AðTÞ ¼ L1 ½sI F1 t¼T ¼
cos !T sin !T ð T 1 cos !T F bðTÞ ¼ e gd ¼ sin !T 0
12.3.5
sin !T cos !T
Analysis of Sampled-Data Systems
1 Analysis Based on the State Equations To solve Eqs (12.3-31), we take advantage of the results of Subsec. 12.2.3, since they differ only by the constant T in Eqs (12.3-31). We therefore have that the general solution of Eq. (12.3-31a) is given by xðkTÞ ¼ r½ðk k0 ÞTxðk0 TÞ þ
k1 X
r½ðk i 1ÞTBðTÞuðiTÞ
ð12:3-34aÞ
i¼k0
and the general solution of Eq. (12.3-31b) by yðkTÞ ¼ Cr½ðk k0 ÞTxðk0 TÞ þ C
k1 X
r½ðk i 1ÞTBðTÞuðiTÞ þ DuðkTÞ
i¼k0
ð12:3-34bÞ where r½ðk k0 ÞT is the transition matrix, given by the relation r½ðk k0 ÞT ¼ ½AðTÞkk0 Clearly, if we set T ¼ 1 in Eqs (12.3-34a and b), then we obtain the formulas (12.2-16) and (12.2-17), respectively. 2 Analysis Based on HðkT Þ Consider the continuous-time system shown in Figure 12.15, where the two samplers are synchronized. The output vector yðkTÞ, i.e., the vector yðtÞ at the sampling points t ¼ kT , is
Digital Control
Figure 12.15
yðkTÞ ¼
539
A continuous-time system with input and output samplers. 1 X
HðkT iTÞuðiTÞ
ð12:3-35Þ
i¼0
Equation (12.3-35) represents, as is already known, a convolution. If we set T ¼ 1, then Eq. (12.3-35) is the vector form of the scalar convolution (12.2-12). 3 Analysis Based on HðzÞ If we take the Z-transform of Eq. (12.3-35), we obtain the following expression for the output vector: YðzÞ ¼ HðzÞUðzÞ
ð12:3-36Þ
where YðzÞ ¼ Z½yðkTÞ;
UðzÞ ¼ Z½uðkTÞ;
HðzÞ ¼ Z½HðkTÞ
and
ð12:3-37Þ Example 12.3.6 For the system of Example 12.3.5, for T ¼ 1 and ! ¼ =2, find: (a) The transition matrix rðkTÞ (b) The state-space vector xðkTÞ (c) The output vector yðkTÞ: Solution For the given values of ! and T, A, B, and c become 1 1 0 1 A¼ ; b¼ ; c¼ 0 1 0 1 (a)
rðzÞ ¼ Z½rðkTÞ ¼ zðzI AÞ1 ¼
2 1 z z2 þ 1 z
z z2
Therefore 2
"
# z2 6Z 6 z2 þ 1 6 rðkTÞ ¼ 6 6 4 Z1 z z2 þ 1 1
3 z Z 7 z2 þ 1 7 7 " #7 2 7 z 5 Z 1 2 z þ1 1
Using the tables of Z-transform pairs given in Appendix C, we have
540
Chapter 12
3 kT 2 7 7 kT 5 cos 2
2
kT 6 cos 2 rðkTÞ ¼ 6 4 kT sin 2
sin
z 1 1 h z i 1 z2 þ 1 z z 1 z 1 zðz þ 1Þ 1 ¼ 2 ðz þ 1Þðz 1Þ zðz 1Þ
XðzÞ ¼ ðzI AÞ1 BUðzÞ ¼
ðbÞ
Hence 3 kT 1 cos 6 2 7 7 xðkTÞ ¼ Z 1 ½XðzÞ ¼ 6 4 kT 5 sin 2 2
(c)
Finally, the output of the system is given by yðkTÞ ¼ cT xðkTÞ ¼ ½1
12.4
0xðkT Þ ¼ 1 cos
kT 2
STABILITY
12.4.1
Definitions and Basic Theorems of Stability
1 Introduction Consider the nonlinear discrete-time system xðkT þ TÞ ¼ f½xðkTÞ; kT; uðkTÞ;
xðk0 TÞ ¼ x0
ð12:4-1Þ
Let uðkTÞ ¼ 0, for k k0 . Moreover, let xðkTÞ and x~ ðkTÞ be the solutions of Eq. (12.4-1) when the initial conditions are xðk0 TÞ and x~ ðk0 TÞ, respectively. Also let the symbol k k represent the Euclidean norm
1=2 kxk ¼ x21 þ x22 þ þ x2n We give the following definitions of stability. Definition 12.4.1: Stability The solution xðkTÞ of Eq. (12.4-1) is stable if for a given " > 0 there exists a ð"; k0 Þ > 0 such that all solutions satisfying kxðk0 TÞ x~ ðk0 TÞk < " imply that kxðkTÞ x~ ðkTÞk < for all k k0 . Definition 12.4.2: Asymptotic Stability The solution xðkTÞ of Eq. (12.4-1) is asymptotically stable if it is stable and if kxðkT Þ x~ ðkTÞk ! 0 as k ! þ1, under the constraint that kxðk0 TÞ x~ ðk0 TÞk is sufficiently small.
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In the case where the system (12.4-1) is stable in accordance with Definition 12.4.1, the point xðk0 TÞ is called the equilibrium point. In the case where the system (12.4-1) is asymptotically stable, the equilibrium point is the origin 0. 2 Stability of Linear Time-Invariant Discrete-Time Systems Consider the linear time-invariant discrete-time system xðkT þ TÞ ¼ AxðkTÞ þ BuðkTÞ; xðk0 TÞ ¼ x0 yðkTÞ ¼ CxðkTÞ þ DuðkTÞ
ð12:4-2Þ
Applying Definition 12.4.1 to the system (12.4-2), we have the following theorem. Theorem 12.4.1 System (12.4-2) is stable according to Definition 12.4.1 if, and only if, the eigenvalues i of the matrix A, i.e., the roots of the characteristic equation jI Aj ¼ 0, lie inside the unit circle (i.e., ji j < 1), or the matrix A has eigenvalues on the unit circle (i.e., ji j ¼ 1) of multiplicity one. Applying Definition 12.4.2 to the system (12.4-2), we have the following theorem. Theorem 12.4.2 System (12.4-2) is asymptotically stable if, and only if, limk!1 xðkTÞ ¼ 0, for every xðk0 TÞ, when uðkTÞ ¼ 0 ðk k0 Þ. On the basis of Theorem 12.4.2, we prove the following theorem. Theorem 12.4.3 System (12.4-2) is asymptotically stable if, and only if, the eigenvalues i of A are inside the unit circle. Proof The theorem will be proved for the special case where the matrix A has distinct eigenvalues. The proof of the case where the eigenvalues are repeated is left as an exercise. When uðkTÞ ¼ 0 ðk k0 Þ, the state vector xðkTÞ is given by xðkTÞ ¼ Akk0 xðk0 TÞ
ð12:4-3Þ
Let the eigenvalues 1 ; 2 ; . . . ; n of the matrix A be distinct. Then, according to the Sylvester theorem (see Sec. 2.12), the matrix Ak can be written as Ak ¼
n X
Ai ki
ð12:4-4Þ
i¼1
where Ai are special matrices which depend only on A. Substituting Eq. (12.4-4) in Eq. (12.4-3), we obtain xðkTÞ ¼
n X i¼1
Hence
0 Ai kk xðk0 TÞ i
ð12:4-5Þ
542
Chapter 12
" lim xðkTÞ ¼
k!1
n X i¼1
#
Ai xðk0 TÞ lim
k!1
0 kk i
ð12:4-6Þ
From Eq. (12.4-6) it is obvious that limk!1 xðkT Þ ¼ 0, 8xðk0 TÞ if, and only if, 0 ¼ 0; 8i ¼ 1; 2; . . . ; n, which is true if and only if limk!1 kk i ji j < 1; 8i ¼ 1; 2; . . . ; n, where j j stands for the magnitude of a complex number. A comparison between linear time-invariant continuous-time systems and linear time-invariant discrete-time systems with respect to the asymptotic stability is shown in Figure 12.16. 3 Bounded-Input Bounded-Output Stability Definition 12.4.3 A linear time-invariant system is bounded-input–bounded-output (BIBO) stable if a bounded input produces a bounded output for every initial condition. Applying Definition 12.4.3 to system (12.4-2), we have the following theorem. Theorem 12.4.4 The linear time-invariant system (12.4-2) is BIBO stable if, and only if, the poles of the transfer function HðzÞ ¼ CðzI AÞ1 B þ D, before any pole-zero cancellation, are inside the unit circle.
Figure 12.16 time systems.
A comparison of asymptotic stability between continuous-time and discrete-
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From Definition 12.4.3 we may conclude that asymptotic stability is the strongest, since it implies both stability and BIBO stability. It is easy to give examples showing that stability does not imply BIBO stability and vice versa.
12.4.2 Stability Criteria The concept of stability has been presented in some depth in the preceding subsection. For testing stability, various techniques have been proposed. The most popular techniques for determining the stability of a discrete-time system are the following: 1. 2. 3. 4. 5.
The The The The The
Routh criterion, using the Mo¨bius transformation Jury criterion Lyapunov method root locus method Bode and Nyquist criteria
Here, we briefly present criteria 1 and 2. For the rest of the stability criteria see [3, 11, 13]. 1 The Routh Criterion Using the Mo¨bius Transformation Consider the polynomial aðzÞ ¼ a0 zn þ a1 zn1 þ þ an
ð12:4-7Þ
The roots of the polynomial are the roots of the equation aðzÞ ¼ a0 zn þ a1 zn1 þ þ an ¼ 0
ð12:4-8Þ
As stated in Theorem 12.4.3, asymptotic stability is secured if all the roots of the characteristic polynomial lie inside the unit circle. The well-known Routh criterion for continuous-time systems is a simple method for determining if all the roots of an arbitrary polynomial lie in the left complex plane without requiring the determination of the values of the roots. The Mo¨bius bilinear transformation w¼
zþ1 z1
or
z¼
wþ1 w1
ð12:4-9Þ
maps the unit circle of the z-plane into the left w-plane. Consequently, if the Mo¨bius transformation is applied to Eq. (12.4-8), then the Routh criterion may be applied, as in the case of continuous-time systems. Example 12.4.1 The characteristic polynomial aðzÞ of a system is given by aðzÞ ¼ z2 þ 0:7z þ 0:1. Investigate the stability of the system. Solution Applying the transformation (12.4-9) to aðzÞ yields
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Chapter 12
wþ1 2 wþ1 ðw þ 1Þ2 þ 0:7ðw2 1Þ þ 0:1ðw 1Þ2 þ 0:1 ¼ aðwÞ ¼ þ0:7 w1 w1 ðw 1Þ2 ¼
1:8w2 þ 1:8w þ 0:4 ðw 1Þ2
The numerator of aðwÞ is called the ‘‘auxiliary’’ characteristic polynomial to which the well-known Routh criterion will be applied. For the present example, the Routh array is 0:4 w2 1:8 1 1:8 0 w 0 w0 0:4 The coefficients of the first column have the same sign and, according to Routh’s criterion, the system is stable. We can reach the same result if we factorize aðzÞ into a product of terms, in which case aðzÞ ¼ ðz þ 0:5Þðz þ 0:2Þ. The two roots of aðzÞ are 0:5 and 0:2, which are both inside the unit circle and hence the system is stable. 2 The Jury Criterion It is useful to establish criteria which can directly show whether a polynomial aðzÞ has all its roots inside the unit circle instead of determining its eigenvalues. Such a criterion, which is equivalent to the Routh criterion for continuous-time systems, has been developed by Schur, Cohn, and Jury. This criterion is usually called the Jury criterion and is described in detail below. First, the Jury table is formed for the polynomial aðzÞ, given by Eq. (12.4-7), as shown in Table 12.3. The first two rows of the table are the coefficients of the polynomial aðzÞ presented in the forward and reverse order, respectively. The third row is formed by multiplying the second row by n ¼ an =a0 and subtracting the result from the first row. Note that the last element of the third row becomes zero. The fourth row is identical to the third row, but in reverse order. The above procedure is repeated until the 2n þ 1 row is reached. The last row consists of only a single element. The following theorem holds. Theorem 12.4.5: The Jury Stability Criterion If a0 > 0, then the polynomial aðzÞ has all its roots inside the unit circle if, and only if, all ak0 , k ¼ 0; 1; . . . ; n 1, are positive. If all coefficients ak0 differ from zero, then Table 12.3
The Jury Table
a0
a1
an1
an
an
an1
a1
a0
a0n1 n1 an1
an1 1 an1 n2
an1 n1 an1 0
.. . a00
n ¼ an =a0
n1 n1 ¼ an1 n1 =a0
where an1 ¼ aki k akk1 and k ¼ akk =ak0 i
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the number of negative coefficients ak0 is equal to the number of roots which lie outside the unit circle. Remark 12.4.1 If all coefficients ak0 , k ¼ 1; 2; . . . ; n, are positive, then it can be shown that the condition a00 > 0 is equivalent to the following two conditions: að1Þ > 0
ð12:4-10aÞ
n
ð1Þ að1Þ > 0
ð12:4-10bÞ
Relations (12.4-10a and b) present necessary conditions for stability and may therefore be used to check for stability, prior to construction of the Jury table. Example 12.4.2 Consider the characteristic polynomial aðzÞ ¼ z3 1:3z2 0:8z þ 1. Investigate the stability of the system. Solution Condition (12.4-10a) is first examined. Here að1Þ ¼ 1 1:3 0:8 þ 1 ¼ 0:1 and, since að1Þ < 0, the necessary condition (12.4-10a) is not satisfied. Therefore, one or more roots of the characteristic polynomial lie outside the unit circle. It is immediately concluded that the system must be unstable. Example 12.4.3 Consider the second-order characteristic Investigate the stability of the system.
polynomial
aðzÞ ¼ z2 þ a1 z þ a2 .
Solution The Jury table is formed as shown in Table 12.4. All the roots of the characteristics polynomial are inside the unit circle if
1 a2 1 a22 > 0 and ð1 þ a2 Þ2 a21 > 0 1 þ a2 which lead to the conditions 1 < a2 < 1, a2 > 1 þ a1 , and a2 > 1 a1 . The stability region of the second-order characteristic polynomial is shown in Figure 12.17.
Table 12.4
The Jury Table for Example 12.4.3
1
a1
a2
a2
a1
1
1 a22 a1 ð1 a2 Þ
a1 ð1 a2 Þ 1 a22
2 ¼ a2
1 ¼ 1 a22
a21 ð1 a2 Þ 1 þ a2
a1 1 þ a2
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Figure 12.17
The stability region for Example 12.4.3.
Example 12.4.4 Consider the characteristic polynomial aðzÞ ¼ z3 þ Kz2 þ 0:5z þ 2. Find the values of the constant K for which all the roots of the polynomial aðzÞ lie inside the unit circle. Solution The Jury table is formed as shown in Table 12.5. According to Theorem 12.4.5, all ak0 elements are positive, except for the last element which may become positive for certain values or for a range of values of K. This last element in the Jury table may be written as 1 3 ð1
KÞ2 13 ð2K þ 1Þ2 ð1 KÞ ¼ 13 ð1 KÞ 1 K ð2K þ 1Þ2 ¼ 13 ð1 KÞð4K 2 5KÞ ¼ 13 ð1 KÞðKÞð4K þ 5Þ
The last product of terms becomes positive if the following inequalities are true: K > 54 and K < 0. Hence, when 54 < K < 0, the characteristic polynomial has all its roots inside the unit circle and the system under consideration is stable.
12.5
CONTROLLABILITY AND OBSERVABILITY
12.5.1
Controllability
Simply speaking, controllability is a property of a system which is strongly related to the ability of the system to go from a given initial state to a desired final state within a finite time (see Sec. 5.6). Consider the system xðk þ 1Þ ¼ AxðkÞ þ BuðkÞ; yðkÞ ¼ CxðkÞ
xð0Þ ¼ x0
ð12:5-1aÞ ð12:5-1bÞ
The state xðkÞ at time k is given by Eq. (12.2-16), which can be rewritten as follows:
1 3 ð1
1 3 ð2K
þ 1Þ ð1 KÞ 2
2 3 ð1
KÞðK 1Þ ðK 1Þ 2 1 3 ð1 KÞ
K 1 K1
1 0:25 0:5 0:5K 2
2
1
1 3 ð1
K
1
The Jury Table for Example 12.4.4
KÞ KÞðK 1Þ ðK 1Þ
KÞ
2
2 3 ð1
Table 12.5
0
0:5 0:5K 0:75
K
0:5 1
0:5
1 ¼ 2K þ 1
2 ¼ 23 ð1 KÞ
3 ¼ 0:5
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Chapter 12
3 uðk 1Þ 6 uðk 2Þ 7 . . . 7 6 xðkÞ ¼ Ak xð0Þ þ ½B .. AB .. .. Ak1 B6 7 .. 5 4 . 2
ð12:5-2Þ
uð0Þ Definition 12.5.1 Assume that jAj 6¼ 0. Then system (12.5-1a) is controllable if it is possible to find a control sequence fuð0Þ; uð1Þ; . . . ; uðq 1Þg which allows the system to reach an arbitrary final state xðqÞ ¼ m 2 Rn , within a finite time, say q, from any initial state xð0Þ. According to Definition 12.5.1, Eq. (12.5-2) takes on the form 3 2 uðq 1Þ 6 uðq 2Þ 7 . . . 7 6 ð12:5-3Þ m Aq xð0Þ ¼ ½B .. AB .. .. Aq1 B6 7 .. 5 4 . uð0Þ This relation is an inhomogenous algebraic system of equations, having the control sequence fuð0Þ; uð1Þ; . . . ; uðq 1Þg and the parameter q as unknowns. From linear algebra, it is known that this equation has a solution if and only if . . . . rank½B .. .. Aq1 B j m Aq xð0Þ ¼ rank½B .. .. Aq1 B This condition, for every arbitrary final state xðqÞ ¼ m, holds true if and only if . . . rank½B .. AB .. .. Aq1 B ¼ n;
q2N
ð12:5-4Þ
Clearly, an increase in time q improves the possibility of satisfying condition (12.5-4). However, the Cayley–Hamilton theorem (Sec. 2.11) states that the terms A j B, for j n, are linearly dependent on the first n terms (i.e., on the terms B; AB; . . . ; An1 BÞ. Thus, condition (12.5-4) holds true if and only if q ¼ n, i.e., if . . . rank½B .. AB .. .. An1 B ¼ n
ð12:5-5Þ
Therefore, the following theorem has been proved. Theorem 12.5.1 System (12.5-1a) is controllable if and only if rank S ¼ n;
where
S ¼ ½B
AB
An1 B
ð12:5-6Þ
Here the n nm matrix S is called the controllability matrix (see also Subsec. 5.6.1). Remark 12.5.1 For a controllable system of order n, n time units are sufficient for the system to reach any final state m ¼ xðnÞ. Example 12.5.1 Consider the system (12.5-1a), where
Digital Control
A¼
1 0:25
549
1 ; 0
b¼
1 ; 0:5
and
xð0Þ ¼
2 2
Find a control sequence, if it exists, such that xT ð2Þ ¼ ½0:5 xT ð2Þ ¼ ½0:5 1.
1 and
Solution From Eq. (12.5-2), for k ¼ 2, we have xð2Þ ¼ A2 xð0Þ þ Abuð0Þ þ buð1Þ For the first case, where xT ð2Þ ¼ ½0:5 1, the above equation yields 0:5 3:5 1 ½0:5uð0Þ þ uð1Þ ¼ þ 1 1 0:5 This equation leads to the scalar equation 0:5uð0Þ þ uð1Þ ¼ 4. A possible control sequence would be uð0Þ ¼ 2 and uð1Þ ¼ 3. For the second case, where xT ð2Þ ¼ ½0:5 1, we have 0:5 3:5 1 ¼ þ ½0:5uð0Þ þ uð1Þ 1 1 0:5 This equation does not possess a solution. Of course, this occurs because the system is uncontrollable, since rank S ¼ 1, where .. 1 0:5 S ¼ ½b . Ab ¼ 0:5 0:25 Therefore, when the system is uncontrollable, it is not possible for the state vector to reach any preassigned value. Example 12.5.2 Consider the system (12.5-1a), where 0 1 1 ; and ; b¼ A¼ 1 0 1
0 xð0Þ ¼ 0
Find a control sequence, if it exists, that can drive the system to the desired final state m ¼ ½1 1:2T . Solution The controllability matrix of the system is .. 0 1 S ¼ ½b . Ab ¼ 1 1 Here, jSj 6¼ 0. Hence, the system is controllable and therefore there exists a control sequence that can drive the system to the desired final state m ¼ ½1 1:2T . The response of the system at time k ¼ 2 is 0 1 uð0Þ xð2Þ ¼ buð1Þ þ Abuð0Þ ¼ uð1Þ þ uð0Þ ¼ 1 1 uð1Þ þ uð0Þ
550
Chapter 12
For m ¼ xð2Þ, we obtain uð0Þ ¼ 1 and uð1Þ ¼ 0:2. Thus, the desired control sequence is fuð0Þ; uð1Þg ¼ f1; 0:2g. 12.5.2
Observability
Definition 12.5.2 System (12.5-1) is observable if there exists a finite time q such that, on the basis of the input sequence fuð0Þ; uð1Þ; . . . ; uðq 1Þg and the output sequence fyð0Þ; yð1Þ; . . . ; yðq 1Þg, the initial state xð0Þ of the system may be uniquely determined. Consider the system (12.5-1). The influence of the input signal uðkÞ on the behavior of the system can always be determined. Therefore, without loss of generality, we can assume that uðkÞ ¼ 0. We also assume that the output sequence fyð0Þ; yð1Þ; . . . ; yðq 1Þg is known (for a certain q). This leads to the following system of equations: 3 2 2 3 yð0Þ C 6 yð1Þ 7 6 CA 7 7 6 . 7xð0Þ ¼ 6 ð12:5-7Þ 7 6 .. 4 .. 5 5 4 . q1 CA yðq 1Þ where use was made of Eq. (12.2-17) with uðkÞ ¼ 0. Equation (12.5-7) is an inhomogenous linear algebraic system of equations with xð0Þ unknown. Equation (12.5-7) has a unique solution for xð0Þ (as is required from Definition 12.5.2) if, and only if, there exists a finite q such that 2 3 C 6 CA 7 7 rank6 ð12:5-8Þ 4 ... 5 ¼ n CAq1
Clearly, an increase in time q improves the possibility of satisfying condition (12.5-8). However, the Cayley–Hamilton theorem (Sec. 2.11) states that the terms CA j , for j n, are linearly dependent on the first n terms (i.e., on the terms C; CA; . . . ; CAn1 Þ. Thus, condition (12.5-8) holds true if, and only if, q ¼ n, i.e., if 3 2 C 6 CA 7 7 ð12:5-9Þ rank6 4 ... 5 ¼ n CAn1
Therefore, the following theorem has been proved. Theorem 12.5.2 System (12.5-1) is observable if, and only if, 2 3 C 6 CA 7 7 rank R ¼ n; where R¼6 4 ... 5 CAn1
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Here the np n matrix R is called the observability matrix (see also Subsec. 5.6.3). Remark 12.5.3 In an observable system of order n, the knowledge of the first n output values fyð0Þ; yð1Þ; . . . ; yðn 1Þg is sufficient to determine the initial condition xð0Þ of the system uniquely. Example 12.5.3 Consider the system (12.5-1), where uðkTÞ ¼ 0; 8k, and 1 0 0 A¼ ; c¼ 1 1 1 The output sequence of the system is fyð0Þ; yð1Þg ¼ f1; 1:2g. Find the initial state xð0Þ of the system. Solution The observability matrix R of the system is T 0 1 c R¼ T ¼ 1 1 c A which has a nonzero determinant. Hence, the system is observable and the initial conditions may be determined from Eq. (12.5-7) which, for the present example, is 1 0 1 x1 ð0Þ ¼ 1:2 1 1 x2 ð0Þ From this equation, we obtain x2 ð0Þ ¼ 1 and x1 ð0Þ ¼ 0:2. 12.5.3 Loss of Controllability and Observability Due to Sampling As we already know from Sec. 12.3, when sampling a continuous-time system, the resulting discrete-time system matrices depend on the sampling period T. How does this sampling period T affect the controllability and the observability of the discretized system? For a discretized system to be controllable, it is necessary that the initial continuous-time system be controllable. This is because the control signals of the sampled-data system are only a subset of the control signals of the continuoustime system. However, the controllability may be lost for certain values of the sampling period. Hence, the initial continuous-time system may be controllable, but the equivalent discrete-time system may not. Similar problems occur for the observability of the system. Example 12.5.4 The state equations of the harmonic oscillator with HðsÞ ¼ !2 =ðs2 þ !2 Þ are xðtÞ ¼ 0 ! xðtÞ þ 0 uðtÞ ! 0 ! yðtÞ ¼ ½1
0xðtÞ
552
Chapter 12
Investigate the controllability and the observability of the sampled-data (discretetime) system whose states are sampled with a sampling period T. Solution The discrete-time model of the harmonic oscillator is (see Example 12.3.5) cos !T sin !T 1 cos !T xðkT þ TÞ ¼ xðkTÞ þ uðkTÞ sin !T cos !T sin !T yðkTÞ ¼ ½1 0xðkTÞ One can easily calculate the determinants of the controllability and observability matrices to yield jSj ¼ sin !Tð1 cos !TÞ and jRj ¼ sin !T, respectively. We observe that the controllability and observability of the discrete-time system is lost when !T ¼ q, where q is an integer, although the respective continuous-time system is both controllable and observable. 12.6
CLASSICAL AND DISCRETE-TIME CONTROLLER DESIGN
The classical discrete-time controller design methods are categorized as indirect and direct techniques. 1 Indirect Techniques Using these techniques, a discrete-time controller Gc ðzÞ is determined indirectly as follows. Initially, the continuous-time controller Gc ðsÞ is designed in the s-domain, using well-known classical techniques (e.g., root locus, Bode, Nyquist, etc.). Then, based on the continuous-time controller Gc ðsÞ, the discrete-time controller Gc ðzÞ may be calculated using one of the discretization techniques presented in Subsec. 12.3.3. The indirect techniques are presented in Sec. 12.7 that follows. 2 Direct Techniques These techniques start by deriving a discrete-time mathematical model of the continuous-time system under control. Subsequently, the design is carried out in the zdomain, wherein the discrete-time controller Gc ðzÞ is directly determined. The design in the z-domain may be done either using the root locus (Sec. 12.8) or the Bode and Nyquist diagrams (Sec. 12.9). Special attention is given to the PID discrete-time controller design (Sec. 12.10). In Sec. 12.11, a brief description of the steady errors appearing in discretetime systems is presented. 12.7 12.7.1
DISCRETE-TIME CONTROLLERS DERIVED FROM CONTINUOUS-TIME CONTROLLERS Discrete-Time Controller Design Using Indirect Techniques
The practicing control engineer has often greater knowledge and experience in designing continuous-time rather than discrete-time controllers. Moreover, many practical systems already incorporate a continuous-time controller which we desire to replace by a discrete-time controller.
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The remarks above are the basic motives for the implementation of indirect design techniques for discrete-time controllers mentioned in Sec. 12.6. Indirect techniques take advantage of the knowledge and the experience one has for continuoustime systems. Furthermore, in cases where a continuous-time controller is already incorporated in the system under control, it facilitates the design of a discrete-time controller. Consider the continuous-time closed-loop control system shown in Figure 12.18 and the discrete-time closed-loop control system shown in Figure 12.19. The indirect design technique for the design of a discrete-time controller may be stated as follows. Let the specifications of the closed-loop systems shown in Figures 12.18 and 12.19 be the same. Assume that a continuous-time controller Gc ðsÞ, satisfying the specifications of the closed-loop system shown in Figure 12.18, has already been determined. Then, the discrete-time controller Gc ðzÞ shown in Figure 12.19 may be calculated from the continuous-time controller Gc ðsÞ of Figure 12.18, using the discretization techniques presented in Subsec. 12.3.3. 12.7.2 Specifications of the Time Response of Continuous-Time Systems In this section, a brief review of the specifications of the time response of the continuous-time systems is given. These specifications are useful for the material that follows and, as it is usually done, refer to the step response of a second-order system (see also Sec. 4.3). 1 Overshoot One of the basic characteristics of the transient response of a system is the overshoot v, which depends mainly on the damping factor . In the case of a second-order system, without zeros, i.e., for a system with a transfer function of the form !n HðsÞ ¼ 2 ð12:7-1Þ s þ 2!n s þ !2n it is approximately true [see also Eq. (4.3-12)] that " # Overshoot percentage ¼ %v ¼ 100 exp pffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 100 1 0:6 1 2
ð12:7-2Þ
where !n is the natural frequency of the system. Therefore, for a desired overshoot percentage, the damping ratio would be
Figure 12.18
Continuous-time closed-loop control system.
554
Chapter 12
Figure 12.19
Discrete-time closed-loop control system.
%v ð0:6Þ 1 100
ð12:7-3Þ
2 Rise Time Another property which is of interest is the rise time Tr , which is defined as the time required for the response of the system to rise from 0.1 to 0.9 of its final value. For all values of around 0.5, the rise time is approximately given by Tr ffi 1:8=!n
ð12:7-4Þ
Hence, satisfying the above relation for the rise time, the natural frequency !n should satisfy the condition !n 1:8=Tr
ð12:7-5Þ
3 Settling Time Finally, another significant characteristic of the response in the time domain is the settling time Ts , which is defined as the time required for the response to remain close (i.e., within a small error) to the final value. The settling time Ts is given by the relation Ts ¼ =!n
ð12:7-6Þ
where is a constant. It is mentioned that in the case of an error tolerance of about 1%, the constant takes on the value 4.6, whereas in the case of an error tolerance of about 2%, the constant takes on the value 4. Hence, if we desire that the settling time be smaller than a specified value and for an error tolerance of about 1%, then !n 4:6=Ts
ð12:7-7Þ
Remark 12.7.1 Theorem B.3.1 of Appendix B requires that the sampling frequency f be at least twice the highest frequency of the frequency spectrum of the continuous-time input signal. In practice, for a wide class of systems, the selection of the sampling period T ¼ 1=f is made using the following approximate method: let q be the smallest time constant of the system; then, T is chosen such that T 2 ½0:1q; 0:5q.
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Example 12.7.1 Consider the position control servomechanism described in Subsec. 3.13.2. For simplicity, let La ’ 0, Kp ¼ 1, and K ¼ A ¼ 1 and B ¼ 2. Then, the transfer function of the motor–gear–load system becomes Gp ðsÞ ¼ 1=sðs þ 2Þ. To this servomechanism, a continuous-time controller Gc ðsÞ is introduced, as shown in Figure 12.20, which satisfies certain design requirements. Of course, if a discrete-time controller Gc ðzÞ is introduced instead of the continuous-time controller Gc ðsÞ, then the closed-loop system would be as shown in Figure 12.21. Let the continuous-time controller Gc ðsÞ satisfying the design requirements have the following form: sþa sþ2 Gc ðsÞ ¼ Ks ¼ 101 ð12:7-8Þ sþb s þ 6:7 Then the problem at hand is to determine Gc ðzÞ of the closed-loop system shown in Figure 12.21 satisfying the same design requirements, where the sampling time T ¼ 0:2 sec. Solution The transfer function HðsÞ of the closed-loop system of Figure 12.20 is y ðsÞ Gc ðsÞGp ðsÞ 101ðs þ 2Þ ¼ ¼ r ðsÞ 1 þ Gc ðsÞGp ðsÞ sðs þ 2Þðs þ 6:7Þ þ 101ðs þ 2Þ 101s þ 202 ¼ 3 s þ 8:7s2 þ 114:4s þ 202
HðsÞ ¼
ð12:7-9Þ
To find Gc ðzÞ of the closed-loop system of Figure 12.21, it is sufficient to discretize Gc ðsÞ given in Eq. (12.7-8). To this end, we will use the method of polezero matching presented in Subsec. 12.3.3 [relations (12.3-20)–(12.3-23)]. According to this method, Gc ðzÞ has the form z þ z1 ð12:7-10Þ Gc ðzÞ ¼ Kz z þ p1 where the pole s ¼ 6:7 of Gc ðsÞ is mapped into the pole z ¼ p1 of Gc ðzÞ and the zero s ¼ 2 of Gc ðsÞ is mapped into the zero z ¼ z1 of Gc ðzÞ. That is, we have that
Figure 12.20 mechanism.
Continuous-time closed-loop control system of the position control servo-
556
Chapter 12
Figure 12.21
Discrete-time closed-loop control system of the position control servo-
mechanism.
p1 ¼ ebT ¼ e6:7ð0:2Þ ¼ e1:34 ¼ 0:264 z1 ¼ eaT ¼ e2ð0:2Þ ¼ e0:4 ¼ 0:67 The constant Kz of Eq. (12.7-10) is calculated so that the zero frequency amplification constants of Gc ðzÞ and Gc ðsÞGh ðsÞ are the same, i.e., so that the following holds (see relation (12.3-23)): 1 0:67 0þ2 2 Gc ðz ¼ 1Þ ¼ Kz ¼ Gc ðs ¼ 0ÞGh ðs ¼ 0Þ ¼ 101 1 0:264 0 þ 6:7 0 þ 10 Thus Kz ¼ 13:6. It is noted that in the relation above the value of Gh ðsÞ for s ¼ 0 was taken into consideration to obtain the total zero frequency amplification for the continuous-time controller. Hence z 0:67 Gc ðzÞ ¼ 13:6 ð12:7-11Þ z 0:264 In what follows, the responses of the closed-loop systems of Figures 12.20 and 12.21 are compared. To this end, the discrete-time transfer function of G^ ðsÞ ¼ Gh ðsÞ Gp ðsÞ is determined for T ¼ 0:2. We have (" # ) 0:2s 1 e 1 G^ ðzÞ ¼ ZfGh ðsÞGp ðsÞg ¼ Z sðs þ 2Þ s 0:5 0:25 0:25 1 þ ¼ 1 z1 Z 2 ¼ 1 z1 Z 2 s sþ2 s ðs þ 2Þ s z1 0:1 0:25z 0:25z þ ¼ z ðz 1Þ2 z 1 z e0:4 0:0176ðz þ 0:876Þ ð12:7-12Þ ¼ ðz 1Þðz 0:67Þ The transfer function of the closed-loop system of Figure 12.21 would be HðzÞ ¼ ¼
Gc ðzÞG^ ðzÞ 0:239z1 ð1 þ 0:876z1 Þ ¼ 1 0:264z1 1 z1 þ 0:239z1 1 þ 0:876z1 1 þ Gc ðzÞG^ ðzÞ 0:239z1 þ 0:209z2 1 1:025z1 þ 0:473z2
ð12:7-13Þ
Digital Control
Figure 12.22
557
Step response of the continuous-time closed-loop system of Figure 12.20.
Figures 12.22 and 12.23 show the response of the closed-loop systems of Figures 12.20 and 12.21, respectively, where it can be seen that the two step responses are almost the same.
12.8
CONTROLLER DESIGN VIA THE ROOT LOCUS METHOD
The root locus method is a direct method for determining Gc ðzÞ and is applied as follows. Consider the closed-loop system shown in Figure 12.24. The transfer function HðzÞ of the closed-loop system is HðzÞ ¼
GðzÞ 1 þ GðzÞFðzÞ
ð12:8-1Þ
The characteristic equation of the closed-loop system is 1 þ GðzÞFðzÞ ¼ 0
ð12:8-2Þ
For linear time-invariant systems, the open-loop transfer function GðzÞFðzÞ has the form
Figure 12.23
Step response of the discrete-time closed-loop system of Figure 12.21.
558
Chapter 12
Figure 12.24
Discrete-time closed-loop system. m Y
GðzÞFðzÞ ¼ K
ðz þ zi Þ
i¼1 n Y
ð12:8-3Þ
ðz þ pi Þ
i¼1
Substituting Eq. (12.8-3) in Eq. (12.8-2) yields the algebraic equation n Y
ðz þ pi Þ þ K
i¼1
m Y
ðz þ zi Þ ¼ 0
ð12:8-4Þ
i¼1
Definition 12.8.1 The root locus of the closed-loop system of Figure 12.24 are the loci of (12.8-4) in the z-domain as the parameter K varies from 1 to þ1. Since the poles pi and the zeros zi are, in general, functions of the sampling time T, it follows that for each T there corresponds a root locus of Eq. (12.8-4), thus yielding a family of root loci for various values of T. The construction of the root locus of Eq. (12.8-4) is carried out using the material of Chap. 7. The following example illustrates the construction of the root locus as the parameter K varies from 0 to þ1. It also illustrates the influence on the root locus of the parameter T. Clearly, the influence of the sampling time T on the root locus is a feature which appears in the case of discrete-time systems, but not in the continuous-time systems. Example 12.8.1 Consider the closed-loop system shown in Figure 12.25. Construct the root locus of the system for K > 0 and for several values of the sampling time T. Note that for a ¼ 2, the system under control is the position control system presented in Example 12.7.1. Solution Let G^ ðsÞ ¼ Gh ðsÞGp ðsÞ. Then
1 esT 1 1 1 esT ¼ 2 G^ ðsÞ ¼ sðs þ aÞ s s ðs þ aÞ 1 a 1 1 ¼ 2 2 þ 1 esT s sþa a s
Digital Control
Figure 12.25
559
Discrete-time closed-loop control system of Example 12.8.1.
Hence g^ ðtÞ ¼ L1 fG^ ðsÞg ¼ pðtÞ pðt TÞ ðt TÞ where pðtÞ ¼ L1
1 a 1 1 1 ¼ 2 at 1 þ eat 2 2 s sþa a s a
Therefore g^ ðkTÞ ¼ pðkTÞ pðkT TÞ ðkT TÞ Applying relation (B.3-12b) of Appendix B, we have G^ ðzÞ ¼ Zfg^ðkTÞg ¼ PðzÞ z1 PðzÞ ¼ 1 z1 PðzÞ where 1
PðzÞ ¼ ZfpðkTÞg ¼ 2 Z akT ðkTÞ þ eakT a 1 aTz z z þ ¼ 2 a ðz 1Þ2 z 1 z eaT Therefore
G^ ðzÞ ¼ ZfG^ ðsÞg ¼ ZfGh ðsÞGp ðsÞg ¼ Zfg^ ðkTÞg ¼ 1 z1 PðzÞ aTz 1 z z 1 þ ¼ 2 1z a ðz 1Þ2 z 1 z eaT
3 2 1 aTeaT eaT
z þ 1 þ aT þ eaT 7 aT þ eaT 1 6 7 6 ¼ 7 6 5 4 a2 z 1Þðz eaT
or G^ ðzÞ ¼ K0 where
z þ z1 Þ ðz 1Þðz p1 Þ
560
Chapter 12
K0 ¼
1 aT þ eaT 1 ; a2
p1 ¼ eaT ;
and
z1 ¼
1 TaeaT eaT 1 þ aT þ eaT
Hence, the open-loop transfer function GðzÞFðzÞ for the present example is K0 ðz þ z1 Þ ^ GðzÞFðzÞ ¼ K GðzÞ ¼ K ðz 1Þðz p1 Þ Clearly, the constant K0 , the pole p1 , and the root z1 are changing with the sampling period T. For this reason, as T changes, so will the root locus of GðzÞFðzÞ. Figure 12.26 presents the root loci for three different values T1 , T2 , and T3 of T, where T1 < T2 < T3 .
Figure 12.26 T1 < T2 < T3 .
Root loci diagrams of the closed-loop system of Figure 12.25 where
Digital Control
561
Next, the special case where a ¼ 1 and T ¼ 1; 2, and 4 sec will be studied. For a ¼ 1 and T ¼ 1, the open-loop transfer function GðzÞFÞzÞ becomes 0:368ðz þ 0:718Þ GðzÞFðzÞ ¼ K ðz 1Þðz 0:368Þ For a ¼ 1 and T þ 2, the open-loop transfer function GðzÞFðzÞ becomes 1:135ðz þ 0:523Þ GðzÞFðzÞ ¼ K ðz 1Þðz 0:135Þ Finally, for a ¼ 1 and T ¼ 4, the open-loop transfer function GðzÞFðzÞ becomes 3:018ðz þ 0:3Þ GðzÞFðzÞ ¼ K ðz 1Þðz 0:018Þ Figure 12.27 presents the root loci for a ¼ 1 and for the three cases of T ¼ 1, 2, and 4 sec. The influence of the parameter T can be observed here in greater detail than in Figure 12.26. Figure 12.27 shows that, for a fixed value of K, an increase in the sampling time T would result in a less stable closed-loop system. On the contrary, a decrease in T results in a more stable system. As a matter of fact, the more the sampling period T goes to zero ðT ! 0Þ, the more the behavior of the closedloop system approaches that of the continuous-time system (here, the continuoustime closed-loop system is stable for all positive values of K). It is also noted that as the value of T increases, the critical value of Kc decreases and vice versa, where by critical value of K we mean that particular value of K where the system becomes unstable. 12.9
CONTROLLER DESIGN BASED ON THE FREQUENCY RESPONSE
12.9.1 Introduction The well-established frequency domain design controller techniques for continuoustime systems (see Chap. 9), can be extended to cover the case of the discrete-time systems. At first, one might think of carrying out this extension by using the relation z ¼ esT . Making use of this relation, the simple and easy-to-use logarithmic curves of the Bode diagrams for the continuous-time case cease to hold for the discrete-time systems (that is why the extension via the relation z ¼ esT is not recommended). To maintain the simplicity of the logarithmic curves for the discrete-time systems, we make use of the following bilinear transformation: 1 þ Tw=2 2 z1 or w¼ z¼ ð12:9-1Þ 1 Tw=2 T zþ1 The transformation of a function of s to a function of z based on the relation z ¼ esT and, subsequently, the transformation of the resulting function of z to a function of w based on the relation (12.9-1), are presented in Figure 12.28. The figure shows that the transformation of the left-half complex plane on the s-plane transforms into the unit circle in the z-plane via the relation z ¼ esT , whereas the unit circle on the zplane transforms into the left-half complex plane in the w-plane, via the bilinear transformation (12.9-1).
562
Chapter 12
Figure 12.27
Root loci diagrams for the closed-loop system 12.25 for a ¼ 1 and T ¼ 1, 2,
and 4 sec.
At first sight, it seems that the frequency responses would be the same in both the s- and the w-domain. This is actually true, with the only difference that the scales of the frequencies w and v are distorted, where v is the (hypothetical or abstract) frequency in the w-domain. This frequency ‘‘distortion’’ may be observed if in Eq. (12.9-1) we set w ¼ jv and z ¼ e j!T , yielding w
¼ jv ¼ w¼jv
2 z 1 2 e j!t 1 2 !T tan ¼ ¼ j T z þ 1 z¼e j!T T e j!T þ 1 T 2
Digital Control
Figure 12.28
Therefore v¼
563
Mappings from the s-plane to the z-plane and from the z-plane to the w-plane.
2 !T tan T 2
ð12:9-2Þ
Since !T !T ð!TÞ3 þ tan ¼ 2 2 8
ð12:9-3Þ
it follows that for small values of !T we have that tanð!T=2Þ ’ !T=2. Substituting this result in Eq. (12.9-2), we have v ’ !;
for small !T
ð12:9-4Þ
Therefore, the frequencies ! and v are linearly related if the product !T is small. For greater !T, Eq. (12.9-4) does not hold true. Figure 12.29 shows the graphical representation of Eq. (12.9-2). It is noted that the frequency range !s =2 ! !s =2 in the s-domain corresponds to the frequency range 1 v 1 in the w-domain, where !s is defined by the relation ð!s =2ÞðT=2Þ ¼ =2. 12.9.2 Bode Diagrams Using the above results, one may readily design discrete-time controllers using Bode diagrams. To this end, consider the closed-loop system shown in Figure 12.30. Then, the five basic steps for the design of Gc ðzÞ are the following: 1. 2.
Determine Determine
G^ ðzÞ from the relation G^ ðzÞ ¼ ZfG^ ðsÞg ¼ ZfGh ðsÞGp ðsÞg G^ ðwÞ using the bilinear transformation (12.9-1), yielding
G^ ðwÞ ¼ G^ ðzÞjz¼ð1þTw=2Þ=ð1Tw=2Þ 3. 4.
ð12:9-5Þ
Set w ¼ jv in G^ ðwÞ and draw the Bode diagrams of G^ ð jvÞ Determine the controller Gc ðwÞ using similar techniques to those applied for continuous-time systems (see Chap. 9)
564
Chapter 12
Figure 12.29
5.
Graphical representation of relation (12.9-2).
Determine Gc ðzÞ from Gc ðwÞ using the bilinear transformation (12.9-1), yielding Gc ðzÞ ¼ Gc ðwÞjw¼ð2=TÞ½ðz1Þ=ðzþ1Þ
ð12:9-6Þ
Note that the specifications for the bandwidth are transformed from the sdomain to the w-domain using relation (12.9-2). Thus, if for example !b is the desired frequency bandwidth, then the design in the w-domain must be carried out for a frequency bandwidth vb , where 2 ! T ð12:9-7Þ vb ¼ tan b T 2
Example 12.9.1 Consider the position servomechanism shown in Figure 12.21 of Example 12.7.1. Find a controller Gc ðzÞ such that the closed-loop system satisfies the following
Figure 12.30
Discrete-time closed-loop system.
Digital Control
565
specifications: gain margin Kg 25 dB, phase margin ’p 708, and velocity error constant Kv ¼ 1 sec1 . The sampling period T is chosen to be 0.1 sec. Solution Let G^ ðsÞ ¼ Gh ðsÞGp ðsÞ. Then
1 eTs 1 1 1 ^ ^ ¼ 1z Z 2 GðzÞ ¼ Z GðsÞ ¼ Z sðs þ 2Þ s s ðs þ 2Þ " # 1 1 þ 0:935z z þ 0:935 ¼ ð0:0047Þ ¼ 0:0047z1 ðz 1Þðz 0:819Þ 1 z1 1 0:819z1 For T ¼ 0:1 sec, the bilinear transformation (12.9-1) becomes z¼
1 þ ðTw=2Þ 1 þ 0:05w ¼ 1 ðTw=2Þ 1 0:05w
Substituting the above transformation in G^ ðzÞ we have 1 þ 0:05w þ 0:935 0:0047 1 0:05w G^ ðwÞ ¼ 1 þ 0:05w 1 þ 0:05w 1 0:8187 1 0:05w 1 0:05w 0:5ð1 þ 0:00167wÞð1 0:05wÞ ¼ wð1 þ 0:5wÞ The gain and phase Bode diagrams of G^ ð jvÞ ¼ G^ ðw ¼ jvÞ are given in Figure 12.31. We choose the following form for the controller Gc ðwÞ: 1 þ aw Gc ðwÞ ¼ K 1 þ bw where a and b are constants. The open-loop transfer function is 1 þ aw 0:5ð1 þ 0:00167wÞð1 0:05wÞ Gc ðwÞG^ ðwÞ ¼ K 1 þ bw wð1 þ 0:5wÞ From the definition of the velocity error constant Kv , we have
Kv ¼ lim wGc ðwÞG^ ðwÞ ¼ 0:5K ¼ 1 w!0
and therefore K ¼ 2. The parameters a and b can be determined by applying the respective techniques of continuous-time systems (Chap. 9), which yield a ¼ 0:8 and b ¼ 0:5. Hence 1 þ 0:8w Gc ðwÞ ¼ 2 1 þ 0:5w The open-loop transfer function is 1 þ 0:8w 0:5ð1 þ 0:00167wÞð1 0:05wÞ Gc ðwÞG^ ðwÞ ¼ 2 1 þ 0:5w wð1 þ 0:5wÞ
566
Chapter 12
Figure 12.31
The gain and phase Bode diagrams of G^ ð jvÞ of Example 12.9.1.
Checking the above results, we find that Kg ’ 25:05 dB, ’p ’ 728, and Kv ¼ 1 sec1 . Therefore, the closed-loop design requirements are satisfied. It remains to determine Gc ðzÞ from Gc ðwÞ. To this end, we use the bilinear transformation Eq. (12.9-1) which, for T ¼ 0:1 sec, becomes 2 z1 2 z1 z1 w¼ ¼ ¼ 20 T zþ1 0:1 z þ 1 zþ1 Thus, Gc ðzÞ has the form 3 2 z1 ! 1 þ ð0:8Þð20Þ 1 7 6 z 0:882 1 0:882z z þ 1 7 ¼ 3:09 ¼ 3:09 Gc ðzÞ ¼ 26 4 z1 5 z 0:818 1 0:818z1 1 þ ð0:5Þð20Þ zþ1 12.9.3
Nyquist Diagrams
Consider a closed-loop system with an open-loop transfer function G~ ðzÞ ¼ ZfGðsÞFðsÞg. Since the z- and s-domains are related via the relation z ¼ esT , it follows
Digital Control
567
that the Nyquist diagram of G~ ðzÞ would be the diagram of G~ ðesT Þ, as s traces the Nyquist path. In the z-domain, the Nyquist path is given by the relation z ¼ esT js¼j! ¼ e j!T
ð12:9-8Þ
and, therefore, the Nyquist path in the z-domain is the unit circle. Hence, to apply the Nyquist stability criterion for discrete-time systems, we draw the diagram of G~ ðe j!T Þ having the cyclic frequency ! as a parameter. The following theorem holds. Theorem 12.9.1 Assume that the transfer function ZfGðsÞFðsÞg does not have any poles outside the unit circle. Then, the closed-loop system is stable if the Nyquist diagram of ZfGðsÞFðsÞg, for z ¼ e j!T , does not encircle the critical point ð1; j0Þ. Theorem 12.9.1 is the respective (or equivalent) of the Nyquist theorem for continuous-time systems (Subsec. 8.4.3). Clearly, the study of the stability of discrete-time closed-loop systems, as well as the design of discrete-time controllers, can be accomplished on the basis of the Nyquist diagrams, by extending the known techniques of continuous-time systems as was done in the case of the Bode diagrams in the previous subsection. This extension is straightforward and is not presented here (see for example [6]). 12.10
THE PID CONTROLLER
In discrete-time systems, as in the case of continuous-time systems (see Sec. 9.6), the PID controller is widely used in practice. This section is devoted to the study of discrete-time PID controllers. We will first study separately the proportional (P), the integral (I), and the derivative (D) controller and, subsequently, the composite PID controller. 12.10.1
The Proportional Controller
For the continuous-time systems, the proportional controller is described by the relation uðtÞ ¼ Kp eðtÞ
ð12:10-1aÞ
and therefore Gc ðsÞ ¼ Kp
ð12:10-1bÞ
For the discrete-time systems, the proportional controller is described by the relation uðkÞ ¼ Kp eðkÞ
ð12:10-2aÞ
and therefore Gc ðzÞ ¼ Kp 12.10.2
ð12:10-2bÞ
The Integral Controller
For the continuous-time systems, the integral controller is described by the integral equation
568
Chapter 12
uðtÞ ¼
Kp Ti
ðt
ð12:10-3aÞ
eðtÞ dt t0
and therefore Gc ðsÞ ¼
Kp Ti s
ð12:10-3bÞ
where the constant Ti is called the integration time constant or reset. In the case of discrete-time systems, the integral equation (12.10-3a) is approximated by the difference equation uðkÞ uðk 1Þ Kp ¼ eðkÞ T Ti or uðkÞ ¼ uðk 1Þ þ
Kp T eðkÞ Ti
ð12:10-4aÞ
and therefore Gc ðzÞ ¼
Kp T Ti ð1 zÞ
1
¼
Kp Tz Ti ðz 1Þ
ð12:10-4bÞ
12.10.3 The Derivative Controller For the continuous-time systems, the derivative controller is described by the differential equation uðtÞ ¼ Kp Td e_ðtÞ
ð12:10-5aÞ
and therefore Gc ðzÞ ¼ Kp Td s
ð12:10-5bÞ
where the constant Td is called the derivative or rate time constant. In the case of discrete-time systems, the differential equation (12.10-5a) is approximated by the difference equation eðkÞ eðk 1Þ uðkÞ ¼ Kp Td ð12:10-6aÞ T and therefore Gc ðzÞ ¼ Kp Td
"
# Kp Td z 1 1 z1 ¼ z T T
ð12:10-6bÞ
12.10.4 The Three-Term PID Controller Combining all the above, we have that the PID controller, for continuous-time systems, is described by the integrodifferential equation ð 1 t uðtÞ ¼ Kp eðtÞ þ eðtÞ dt þ Td e_ðtÞ ð12:10-7aÞ T i t0
Digital Control
569
and therefore 1 Gc ðsÞ ¼ Kp 1 þ þ Td s Ti s
ð12:10-7bÞ
Figure 9.21 presents the block diagram of the Gc ðsÞ for the continuous-time PID controller. In the case of discrete-time systems, the PID controller is described by the difference equation " # k1 TX Td ð12:10-8aÞ eðiÞ þ ½eðkÞ eðk 1Þ uðkÞ ¼ Kp eðkÞ þ Ti i¼0 T where the middle term in Eq. (12.10-8a) is the solution of Eq. (12.10-4a). Hence T h z i Td z 1 þ Gc ðzÞ ¼ Kp 1 þ ð12:10-8bÞ Ti z 1 z T After some algebraic manipulations, Gc ðzÞ may be written as " # z2 az þ b Gc ðzÞ ¼ K zðz 1Þ
ð12:10-9Þ
where " K ¼ Kp
TTi þ Td Ti þ T 2 Ti T
#
Ti T Td Ti TTi þ Td Ti þ T 2 Td Ti b¼ TTi þ Td Ti þ T 2 a¼
Figure 12.32 presents the block diagram of the discrete-time PID controller Gc ðzÞ.
Figure 12.32
The block diagram of the discrete-time PID controller.
570
Chapter 12
12.10.5 Design of PID Controllers Using the Ziegler–Nichols Methods The Ziegler–Nichols methods for continuous-time systems have been presented in Subsec. 9.6.5. These methods can be extended directly to the case of discrete-time systems, provided that the sampling is sufficiently fast, i.e., 20 times the highest bandwidth frequency, as is normally the case in practice. If the sampling is not that fast, then the discrete-time PID controller may not produce satisfactory accurate results. 12.11
STEADY-STATE ERRORS
The subject of steady-state errors for the case of continuous-time systems was presented in Sec. 4.7. These results can readily be extended to cover the discrete-time systems case. In the sequel, we briefly cover the subject. Consider the unity feedback discrete-time closed-loop system shown in Figure 12.33. Assume that the system under control is stable (a fact which will allow us to apply the final value theorem given by Eq. (B.3-20) of Appendix B). Define
GðsÞ 1 eTs G^ ðzÞ ¼ Z Gh ðsÞGðsÞ ¼ Z GðsÞ ¼ 1 z1 Z s s Then, the closed-loop transfer function HðzÞ will be HðzÞ ¼
G^ ðzÞ YðzÞ ¼ RðzÞ 1 þ G^ ðzÞ
ð12:11-1Þ
The error EðzÞ is given by EðzÞ ¼ RðzÞ BðzÞ ¼ RðzÞ G^ ðzÞEðzÞ and hence
"
EðzÞ ¼
1 1 þ G^ ðzÞ
# RðzÞ
ð12:11-2Þ
The steady-state error of eðkTÞ, denoted as ess , is defined as ess ¼ lim eðkTÞ ¼ lim ð1 z1 ÞEðzÞ k!1
z!1
ð12:11-3Þ
Relation (12.11-3) is known as the final value theorem, which is defined by the relation (B.3-20) of Appendix B. If Eq. (12.11-2) is substituted in Eq. (12.11-3) then
Figure 12.33
Unity feedback discrete-time closed-loop system.
Digital Control
571
"
" 1
ess ¼ lim ð1 z Þ z!1
1
#
1 þ G^ ðzÞ
# ð12:11-4Þ
RðzÞ
Next, we will consider three particular excitations rðtÞ: namely, the step function, the ramp function, and the acceleration function. 1 Step Function In this case, rðtÞ ¼ 1 or rðkTÞ ¼ 1 and RðzÞ ¼ ZfrðkTÞg ¼ Zf1g ¼
1 1 z1
Substituting the above value of RðzÞ in Eq. (12.11-4) yields " " # " # # 1 1 1 1 1 ; ess ¼ lim ð1 z Þ ¼ lim ¼ 1 ^ ^ z!1 z!1 1 þ Kp 1 þ GðzÞ 1 z 1 þ GðzÞ Kp ¼ lim G^ ðzÞ
ð12:11-5Þ
z!1
where Kp is called the position error constant. 2 Ramp Function In this case rðtÞ ¼ t or rðkTÞ ¼ kT , which is defined by Eq. (B.2-4) of Appendix B. Hence RðzÞ ¼ ZfrðkTÞg ¼ ZfkTg ¼
Tz1 1 z1
2
Substituting the value of RðzÞ in Eq. (12.11-4) yields " ## " #" " # 1 Tz1 T 1 1 ; ess ¼ lim 1 z ¼ ¼ lim 2 1 ^ 1 ^ z!1 z!1 K 1 þ GðzÞ 1 z GðzÞ v 1z " # 1 z1 G^ ðzÞ Kv ¼ lim ð12:11-6Þ z!1 T where Kv is called the velocity error constant. 3 Acceleration Function In this case rðtÞ ¼ 12 t2 or rðkTÞ ¼ 12 ðkTÞ2 and T 2 1 þ z1 z1 1 2 RðzÞ ¼ ZfrðkTÞg ¼ Z ðkTÞ ¼ 3 2 2 1 z1
Substituting the value of RðzÞ in Eq. (12.11-4) yields
572
Chapter 12
"
ess ¼ lim 1 z1 z!1
"
¼ lim
z!1
¼
1 ; Ka
T2 1 2
"
1
#"
1 þ G^ ðzÞ #
## T 2 1 þ z1 z1 3 2 1 z1
G^ ðzÞ " # 2 1 z1 G^ ðzÞ Ka ¼ lim z!1 T2
1z
ð12:11-7Þ
where Ka is called the acceleration error constant. It is remarked that, as in the case of continuous-time systems (see Sec. 4.7), a discrete-time system is called a type j system when its open-loop transfer function G^ ðzÞ has the form 1 aðzÞ ^ GðzÞ ¼ ð12:11-8Þ ðz 1Þ j bðzÞ where aðzÞ and bðzÞ are polynomials in z which do not involve the term ðz 1Þ.
12.12
STATE-SPACE DESIGN METHODS
The results of Chap. 10 on state-space design methods for continuous-time systems may readily be extended to the case of discrete-time systems. Indeed, the problems of pole assignment, input–output decoupling, exact model matching, and state observers may be solved in a similar way for discrete-time systems (see for example [13]).
12.13
OPTIMAL CONTROL
The results of Chap. 11 on optimal control for continuous-time systems may be extended to cover the case of discrete-time systems. However, this extension is not as easy as for the case of the results of Chap. 10. Here, one first has to present the appropriate mathematical background for the study of optimal control problems of discrete-time systems and, subsequently, use this mathematical background to solve the linear regulator and servomechanism problems. Going from continuous-time systems, which are described by differential equations, to discrete-time systems, which are described by difference equations, a basic difference appears in the form of the cost function: the cost function for continuoustime systems is an integral expression, whereas for discrete-time systems it is a summation expression. Applying the principles of calculus of variations, one arrives at the discrete-time Euler–Lagrange equation. With regard to the maximum principle, an analogous treatment leads to the discrete-time Hamiltonian function, and from there to the discrete-time canonical Hamiltonian equations. Using this discretetime mathematical background, one may then solve the discrete-time regulator and servomechanism problems along the same lines as those used for the case of continuous-time systems. For more details see [13].
Digital Control
12.14
573
PROBLEMS
1. A discrete-time system is described by the state-space equations (12.2-8), where
0 1 A¼ ; 6 5
0 b¼ ; 1
1 c¼ ; 1
D¼0
Find the transition matrix, the transfer function, and the response of the system when the input is the unit step sequence. 2. Find the transfer functions of the systems described by the difference equations (a) yðk þ 2Þ þ 5yðk þ 1Þ þ 6yðkÞ ¼ uðk þ 1Þ (b) yðk þ 2Þ yðkÞ ¼ uðkÞ 3. Find the values of the responses yð0Þ, yð1Þ, yð2Þ, and yð3Þ using the convolution method when (a) hðkÞ ¼ 1 ek and uðkÞ ¼ ðkÞ and uðkÞ ¼ k ðkÞ (b) hðkÞ ¼ 1 ek (c) hðkÞ ¼ sin k and uðkÞ ¼ ðkÞ and uðkÞ ¼ e2k (d) hðkÞ ¼ ek where ðkÞ is the unit step sequence (see Eq. (B.2-2) of Appendix B). 4. Consider the continuous-time system (12.3-24), where
1 0 F¼ ; 0 2
1 g¼ ; 1
0 ; c ¼ 1 T
D¼0
Discretize the system, i.e., find the difference equations of the system in state space, when the sampling frequency T ¼ 0:1 sec. 5. Find the equivalent discrete-time transfer function GðzÞ of the continuous-time transfer function GðsÞ ¼ 1=½sðs þ 1Þ preceded by a zero-order hold described by Gh ðsÞ ¼ ð1 eTs Þ=s. Use two approaches: one making use of the Z-transform tables and the other using a time-domain analysis. 6. The block diagram of a digital space vehicle control system is shown in Figure 12.34, where G and F are constants and J is the vehicle moment of inertia (all in appropriate units). Find the discrete-time transfer function of the closed-loop system. 7. A continuous-time process described by the transfer function K=s is controlled by a digital computer, as shown in Figure 12.35. (a)
Find the closed-loop transfer function YðzÞ=RðzÞ, the disturbance-to-output transfer function YðzÞ=DðzÞ, and the open-loop transfer function YðzÞ=EðzÞ. (b) Obtain the steady-state characteristics of the system using the final value theorem. (c) Find the unit step response of the system for K ¼ 20, F ¼ 5, Kc ¼ 1:25, and T ¼ 0:005 sec. 8. A simplified state-space model for the altitude control (roll control) of a spacecraft is
574
Chapter 12
"
x_ 1 ðtÞ x_ 2 ðtÞ
#
" ¼
0
1
0
0
yðtÞ ¼ ½1 0
#"
x1 ðtÞ x2 ðtÞ
#
" þ
0 1=J
# uðtÞ ¼ FxðtÞ þ guðtÞ
x1 ðtÞ ¼ cT xðtÞ x2 ðtÞ
Figure 12.34
A digital space vehicle control system.
Figure 12.35
Block diagram of a computer-controlled process.
Digital Control
575
where x1 x2 u¼ J¼
¼ the roll of the spacecraft in rad ¼ the roll rate in rad/sec the control torque about the roll axis produced by the thrusters in Nm the moment of inertia of the vehicle about the roll axis at the vehicle center of mass in kg m2
The transfer function relating the roll of the spacecraft to the torque input is GðsÞ ¼
YðsÞ 1 ¼ UðsÞ Js2
Find the equivalent discrete-time description of the system with sampling period T. 9. Find all values of K for which the roots of the following characteristic polynomials lie inside the unit circle: (a) z2 þ 0:2z þ K (c) z2 þ ðK þ 0:4Þz þ 1 (e) z3 0:5z2 0:2z þ K
(b) (d) (f)
z2 þ Kz þ 0:4 z3 þ Kz2 þ 2z þ 2 z3 þ ðK þ 1Þz2 0:5z þ 1
10. A magnetic disk drive requires a motor to position a read/write head over tracks of data on a spinning disk. The motor and the head may be approximated by the transfer function GðsÞ ¼
1 sðT1 s þ 1Þ
where T1 > 0. The controller takes the difference of the actual and desired positions and generates an error. This error is discretized with sampling period T, multiplied by a gain K, and applied to the motor with the use of a zero-order hold of period T (see Figure 12.36). Determine the range of values of the gain K, so that the closed-loop discrete-time system is stable. Apply the invariant impulse response method and the Routh criterion. 11. Consider the system given in Figure 12.37. Apply the Jury criterion to determine the range of values of the gain K for which the system is stable. Assume sampling period T ¼ 0:1 sec, 0.2 sec, and 1 sec.
Figure 12.36
Block diagram of disk drive control system of Problem 10.
576
Chapter 12
Figure 12.37
Block diagram of system of Problem 11.
12. Check the stability of the system described by x1 ðk þ 1Þ ¼ x2 ðkÞ x2 ðk þ 1Þ ¼ 2:5x1 ðkÞ þ x2 ðkÞ þ uðkÞ yðkÞ ¼ x1 ðkÞ If the system is unstable, use the output feedback law uðkÞ ¼ gyðkÞ to stabilize it. Determine the range of values of a suitable g. 13. Consider a system described by the state-space equations (12.2-8), where 2 3 3 2 0 0 1 0 0 607 7 6 0 0 1 0 6 7 6 7 and B ¼ 6 7; ðaÞ A ¼ 6 7; 405 4 0 0 0 1 5 4 2 1 0:4 C ¼ ½1 2 1 4 2
0
0
6 ðbÞ A ¼ 4 1 C¼
1
3
7 0 5; 1 0 0:5 0 1 1 1
1
1
0
1
1
2
7 1 5;
T
0
0
1 2
6 ðcÞ A ¼ 4
1
3
1 2
1
2
7 1 5; 0:5
6 B¼4 1 1
2
1 1
3 and
3
7 6 B ¼ 4 0 1 5; 1 0
and
C¼
0
0
1
1
0
0
Investigate the controllability and observability of these systems. For case (c), find the values of the sampling time T, appearing in matrix A, which make the system controllable and/or observable. 14. Consider the continuous-time system of a rotating body described by the dynamical equation
Digital Control
!_ ¼
577
d2 L ¼ dt2 J
where is the position (angle of rotation), ! is the rate of the angle of rotation, L is the externally applied torque, and J is the moment of inertia. If x1 ¼ and x2 ¼ _ ¼ !, then the state-space description is 0 1 x1 0 L x_ 1 ¼ Fx þ gu ¼ þ 0 0 x2 1 J x_ 2 Obtain the discrete-time description using a zero-order hold and a sampling period T. If is taken to be the output, determine if this description is observable. What happens if the angular velocity ! is measured instead? Discuss the results in both cases. 15. A system is described by the state equations xðk þ 1Þ ¼ AxðkÞ þ buðkÞ yðkÞ ¼ cT xðkÞ where A¼
0 1 ; 2 3
b¼
1 ; 1
and
cT ¼ ½1
2
Determine the controllability and observability of both the open-loop and the closed-loop systems when uðkÞ ¼ rðkÞ f T xðkÞ where rðkÞ is some reference input and f ¼ ½ f1 f2 T . 16. Solve Example 12.7.1 with the following specifications: (a) maximum overshoot 10% and natural frequency !n ¼ 2 rad/sec (b) maximum overshoot 20% and natural frequency !n ¼ 6 rad/sec (c) maximum overshoot 10% and natural frequency !n ¼ 6 rad/sec. Note that here one first has to determine Gc ðsÞ satisfying these specifications and, subsequently, determine Gc ðsÞ. 17. Consider the ball and beam system depicted in Figure 12.38a. The beam is free to rotate in the plane of the page about an axis perpendicular to the page, while the ball rolls in a groove along the rod. The control problem is that of maintaining the ball at a desired position by applying an input torque to the beam. A linear model for the system is GðsÞ ¼ 1=s2 , as shown with a PD controller in Figure 12.38b. Obtain an equivalent discrete-time system. The sampling period is T ¼ 0:1 sec. (b) Design a discrete-time controller using the pole-zero matching method. Draw the unit step responses for the continuous- and the discrete-time systems.
(a)
18. Plastic extrusion is an industrial process. The extruders consist of a large barrel divided into several temperature zones with a hopper at one end and a die at the other. The polymer is fed into the barrel from the hopper and is pushed forward
578
Chapter 12
Figure 12.38
Ball and beam closed-loop control system: (a) ball and beam system; (b) block diagram of closed-loop control system.
by a powerful screw. Simultaneously, it is heated while passing through the various temperature zones set to gradually increasing temperatures. The heat produced by the heaters in the barrel, together with the heat released from the friction between the raw polymer and the surfaces of the barrel and the screw, eventually causes the polymer to melt. The polymer is then pushed out from the die. The discrete-time system for the temperature control is shown in Figure 12.39. The transfer function relating the angular velocity of the screw and the output temperature is GðsÞ ¼ e2s =ðs þ 1Þ, i.e., the system is of the first order, incorporating a delay of 2 sec. The sampling period T ¼ 1 sec. Design a PI controller so that the dominant closed-loop poles have a damping ratio ¼ 0:5 and the number of the output samples in a full cycle of the damped
Figure 12.39
Temperature control system for plastic extrusion.
Digital Control
579
sinusoidal response is 10. Find the unit step response of the discrete-time system. Determine the velocity error coefficient Kv and the steady-state error of the output due to a ramp input. 19. A photovoltaic system is mounted on a space station in order to develop the required power. To maximize the energy production, the photovoltaic panels should follow the sun as accurately as possible. The system uses a dc motor and the transfer function of the panel mount and the motor is GðsÞ ¼
1 sðs þ 1Þ
An optical sensor accurately tracks the sun’s position and forms a unity feedback (see Figure 12.40). The sampling period T ¼ 0:2 sec. Find a discrete-time controller such that the dominant closed-loop poles have damping ratio ¼ 0:5 and there are eight output samples in a complete cycle of the damped sinusoidal response. Use the root locus method in the z-plane to determine the transfer function of the required controller. Find the unit step response of the system and the velocity error coefficient Kv . 20. In this problem, the automatic control of a wheelchair will be studied. The automatic wheelchair is specially designed for handicapped people with a disability from the neck down. It consists of a control system which the handicapped person may operate by using his or her head, thus determining the direction as well as the speed of the chair. The direction is determined by a sensor, situated on the head of the handicapped person at intervals of 908, so that the person may choose one of the four possible directions (motions): forward, backward, left, and right. The speed is regulated by another sensor, whose output is proportional to the movement of the head. Clearly, in the present example, the person is part of the overall controller. For simplicity, we assume that the wheelchair, as well as the sensory device on the head, are described by first-order transfer functions, as shown in Figure 12.41a. We also assume that the time delay, which is anticipated to appear in the visual feedback path, is negligible. More specifically, we assume that K1 ¼ 1, K2 ¼ 10, a ¼ 1, b ¼ 2, and FðsÞ ¼ 1. Suppose that we want to introduce a discrete-time controller to the system. Then, the closed-loop system would be as in Figure 12.41b. Find Gc ðzÞ in order for the closed-loop system to have a gain margin Kg 12 dB, a phase margin ’p 508, and an error constant Kv ¼ 4 sec1 . The sampling period is chosen to be 0.1 sec.
Figure 12.40
Discrete-time system for the positioning of the photovoltaic panels.
580
Chapter 12
Figure 12.41
Wheelchair automatic control system: (a) wheelchair closed-loop system using a continuous time controller; (b) wheelchair closed-loop system using a discrete-time controller.
21. Construct the root locus of Example 12.8.1 for the following values of the parameters a and T: a ¼ 4 and T ¼ 0:1, 1, 5, and 10 sec. 22. Consider a continuous-time open-loop system with transfer function GðsÞ ¼ a=sðs þ aÞ. Close the loop with unity feedback and find the position and the velocity error constants. Use a zero-order hold and unity feedback to obtain a discrete-time equivalent system. Determine the new position and velocity error constants and compare with the continuous-time case. K : 23. Consider the system of Figure 12.42 with unity feedback, where GðsÞ ¼ sðs þ aÞ (a) Determine the transfer function YðzÞ=RðzÞ in terms of K, a, and T (sampling period). (b) Determine the root locus and the maximum value of K for a stable response with T ¼ 0:1 sec, 0.5 sec, and 1 sec and a ¼ 2. (c) Find the steady-state error characteristics for a unit step sequence and a ramp sequence for those error values of K and T that yield a stable system response for a ¼ 1 and a ¼ 2.
Digital Control
Figure 12.42
581
Discretized control system.
BIBLIOGRAPHY Books 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
J Ackermann. Sampled Data Control Systems. New York: Springer Verlag, 1985. KJ Astrom, T Hagglund. PID Controllers: Theory, Design and Tuning. North Carolina: Instruments Society of America, 1995. KJ Astrom, B Wittenmark. Computer Controller Systems: Theory and Design. Englewood Cliffs, New Jersey: Prentice Hall, 1997. JA Cadzow. Discrete-Time Systems, an Introduction with Interdisciplinary Applications. Englewood Cliffs, New Jersey: Prentice Hall, 1973. JA Cadzow, HR Martens. Discrete-Time Systems and Computer Control Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1970. GF Franklin, JD Powell, ML Workman. Digital Control of Dynamic Systems. 2nd ed. London: Addison-Wesley, 1990. CH Houpis, GB Lamont. Digital Control Systems. New York: McGraw-Hill, 1985. EI Jury. Sampled Data Control Systems. New York: John Wiley, 1958; 2nd ed. Huntington, New York: Robert E Krieger, 1973. T Kailath. Linear Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1980. P Katz. Digital Control Using Microprocessors. London: Prentice Hall, 1981. BC Kuo. Digital Control Systems. Orlando, Florida: Saunders College Publishing, 1992. K Ogata. Discrete-Time Control Systems. 2nd ed. Englewood Cliffs, New Jersey: Prentice Hall, 1995. PN Paraskevopoulos. Digital Control Systems. London: Prentice Hall, 1996. GH Perdikaris. Computer Controlled Systems, Theory and Applications. London: Kluwer Academic Publishers, 1991. CL Phillips, HT Nagle Jr. Digital Control System Analysis and Design. Englewood Cliffs, New Jersey: Prentice Hall, 1984. JR Ragazzini, GF Franklin. Sampled Data Control Systems. New York: McGraw-Hill, 1980. M Santina, A Stubbersud, G Hostetter. Digital Control System Design. Orlando, Florida: Saunders College Publishing, 1994.
Articles 18.
JG Ziegler, NB Nichols. Optimum settings for automatic controllers. Trans ASME 64:759–768, 1942.
13 System Identification
13.1
INTRODUCTION
A fundamental concept in science and technology is that of mathematical modeling (see Chap. 3, particularly Secs 3.1–3.7). A mathematical model is a very useful, as well as very compact way, of describing the knowledge we have about a process or system. The determination of a mathematical model of a process or system is known as system identification. In control systems, a mathematical model of a process or system is in most cases necessary for the design of the controller. This becomes apparent if we recall that most of the controller design methods covered in previous chapters are based on the model of the system under control, which was assumed known. The model is also necessary for the design of adaptive and robust control systems presented in Chaps 14 and 15. A process or a system may be described by several models, ranging from necessarily very detailed and complex microscopic models to simplistic macroscopic models which facilitate the understanding of the gross characteristics of a system’s performance. Complex microscopic models require a long time to determine and they are mostly used for the detailed control of a system’s performance. Between the two extremes, there exist several different types of models. Clearly, one must be able to choose the suitable type of model for each specific application. There are basically two ways of determining a mathematical model of a system: by implementing known laws of nature or through experimentation on the process. A popular approach to obtaining a model is to combine both ways. Mathematical models may be distinguished as parametric and nonparametric models. Parametric models obviously involve parameters: for example, the coefficients of differential or difference equations, of state equations, and of transfer functions. Nonparametric models do not involve parameters and are usually graphical representations, such as the Nyquist or Bode diagrams of a transfer function or impulse response function. This chapter refers to parametric models. For nonparametric models see for example [6]. Overall, the parametric identification problem reduces to the development of methods which give a good estimate of the parameters of the system model. 583
584
Chapter 13
In particular, in this chapter we deal with the problem of determining mathematical models of linear, time-invariant, single-input–single-output (siso) discretetime systems, described by difference equations. The proposed method for the identification (estimation) of the coefficients (parameters) of a difference equation is experimental and may be briefly described as follows. First, a set of N linear algebraic equations is formulated, where N is the number of measurements. From these equations, one may easily derive the canonical equation whose solution yields the parameter estimate hðNÞ, where h is the vector parameter under identification. If an estimate of the initial conditions of the dynamic equation is also required, then N þ n measurements are taken and hence N þ n equations are produced, where n is the order of the difference equation. An interesting feature of this chapter, is the determination of a recursive algorithm, which allows the estimation of the vector parameter h for N þ 1 measurements, based on the following formula: hðN þ 1Þ ¼ hðNÞ þ h ¼ hðNÞ þ cðNÞ½ yNþ1 uT ðN þ 1ÞhðNÞ
ð13:1-1Þ
where cðNÞ and uðN þ 1Þ are known vector quantities and yNþ1 is the N þ 1 measurement of the output y of the system. This formula shows that for the determination of hðN þ 1Þ one can use the previous estimate hðNÞ plus a corrective term h, which is due to the new N þ 1 measurement, instead of starting the estimation procedure right from the beginning. This algorithm facilitates the numerical part of the problem and constitutes the cornerstone notion and tool for the solution of ON-LINE identification, i.e., the identification which takes place in real time while the system is operating under normal conditions (Sec. 13.3). In contrast, when the identification procedure is desired to take place involving only the first N measurements, it is carried out off-line only once and for this reason it is called OFF-LINE identification or parameter estimation. For relevant references to system identification see [1–23]. Most of the material of this chapter is a condensed version of the identification material reported in [15].
13.2
OFF-LINE PARAMETER ESTIMATION
13.2.1
First-Order Systems
The simple case of a first-order discrete-time system is studied first. Assume that the system under consideration is described by the differential equation yðkÞ þ a1 yðk 1Þ ¼ b1 uðk 1Þ
ð13:2-1Þ
with initial condition yð1Þ. Assume that the system (13.2-1) is excited with an input sequence uð1Þ; uð0Þ; uð1Þ; . . . . As a result, the output of the system has a sequence yð0Þ; yð1Þ; . . . . The identification problem may now be defined as follows. Given the known input sequence uð1Þ; uð0Þ; uð1Þ; . . . as well as the measured output sequence yð0Þ; yð1Þ; . . . ; find an estimate of the system’s parameters a1 and b1 and of the initial condition yð1Þ. To solve the problem, we begin by writing down Eq. (13.2-1) for N þ 1 measurements, i.e., for k ¼ 0; 1; 2; . . . ; N. Consequently, we arrive at the following set of linear algebraic equations:
System Identification
585
yð0Þ þ a1 yð1Þ ¼ b1 uð1Þ
ð13:2-2aÞ
yð1Þ þ a1 yð0Þ ¼ b1 uð0Þ yð2Þ þ a1 yð1Þ ¼ b1 uð1Þ
ð13:2-2bÞ
.. . yðNÞ þ a1 yðN 1Þ ¼ b1 uðN 1Þ The last N equations, i.e., Eqs (13.2-2b), are used for the estimation of the parameters a1 and b1 . Having estimated the parameters a1 and b1 , Eq. (13.2-2a) can be used for the estimation of the initial condition yð1Þ. To this end, define 3 2 yð1Þ 6 yð2Þ 7 7 6 a1 and ; y¼6 h¼ .. 7 7; 6 b1 4 . 5 yðNÞ 2 yð0Þ u ð0Þ 7 6 6 6 7 6 7 6 6 uT ð1Þ 7 6 yð1Þ 7 6 6 7 6 6 6 7 6 r ¼ 6 7 ¼ 6 .. 7 6 6 .. 7 6 6 . . 7 6 6 7 6 6 4 5 4 yðN 1Þ uT ðN 1Þ 2
T
3
uð0Þ uð1Þ .. .
3 7 7 7 7 7 7 7 7 7 7 7 5
ð13:2-3Þ
uðN 1Þ
Using these definitions, Eqs (13.2-2b) can be written compactly as y ¼ rh
ð13:2-4Þ
Equation (13.2-4) is an algebraic system of N equations with two unknowns. It is clear that if the known input and output sequences involve errors due to measurement or noise, then, for every input–output pair fuðkÞ; yðkÞg, there exists an error eðkÞ; thus, Eqs (13.2-2) will take on the form yðkÞ þ a1 yðk 1Þ ¼ b1 uðk 1Þ þ eðkÞ;
k ¼ 0; 1; 2; . . . ; N
ð13:2-5Þ
Consequently Eq. (13.2-4) becomes y ¼ rh þ e
ð13:2-6Þ
where e is the N-dimensional error vector e ¼ ½eð1Þ eð2Þ eðNÞ. For the minimization of the error vector e, the least-squares methods can be aplied. To this end, define the following cost function T
J ¼ eT e ¼
N X
e2 ðkÞ
k¼1
If Eq. (13.2-6) is substituted in Eq. (13.2-7), we obtain
ð13:2-7Þ
586
Chapter 13
J ¼ ðy rhÞT ðy rhÞ Hence @J ¼ 2rT ðy rhÞ @h where the following formula was used (see Subsec. 2.6.2): @ @ T T ½Ah ¼ h A ¼ AT @h @h If we set @J=@h equal to zero, we obtain rT rh ¼ rT y
ð13:2-8Þ
Relation (13.2-8) is known as the canonical equation and has a solution when the matrix rT r is invertible, in which case we have 1 1 r# ¼ ðrT r rT ð13:2-9Þ h ¼ rT r rT y ¼ r# y; where r# is the pseudoinverse of r. The solution (13.2-9) minimizes the cost function (13.2-7). The matrix rT r is symmetrical and has the following form: 3 2 N 1 n1 X X 2 y ðkÞ yðkÞuðkÞ 7 6 7 6 k¼0 k¼0 T 7 6 ð13:2-10aÞ r r¼6 7 N 1 N 1 X 5 4 X 2 yðkÞuðkÞ u ðkÞ k¼0
k0
Moreover, the vector r y has the form 3 2 N X yðkÞyðk 1Þ 7 6 7 6 k¼1 7 rT y ¼ 6 7 6 X 5 4 N yðkÞuðk 1Þ T
ð13:2-10bÞ
k¼1
The estimate of the parameters a1 and a2 is based on Eq. (13.2-9). The initial condition yð1Þ is estimated on the basis of Eq. (13.2-2a), which gives yð1Þ ¼
1 ½b uð1Þ yð0Þ a1 1
where it is assumed that a1 6¼ 0 and uð1Þ is known. Example 13.2.1 A discrete-time system is described by the first-order difference equation yðkÞ þ a1 yðk 1Þ ¼ b1 ðk 1Þ The input and output sequences uðkÞ and yðkÞ, for N ¼ 6, are given in Table 13.1. Estimate the parameters a1 and b1 , as well as the initial condition yð1Þ:
System Identification
Table 13.1 k
587
Input–Output Measurements for Example 13.2.1
1
0
1
2
3
1
1 1
1 1/2
1 3/4
1 5/8
uðkÞ yðkÞ
4 1 11/16
5 1 21/32
6 1 43/64
Solution From Eq. (13.2-10) and for N ¼ 6, we have that 2 3 5 5 X X 2 y ðkÞ yðkÞuðkÞ 7 6 7 6 k¼0 3:106445 k¼0 T 7¼ r r¼6 7 6 X 5 5 X 2 4:21875 5 4 yðkÞuðkÞ u ðkÞ k¼0
2
6 X
4:21875 6
k¼0
3
yðkÞyðk 1Þ 7 6 7 6 k¼1 2:665527 7¼ r y¼6 7 6 X 3:890625 4 6 5 yðkÞuðk 1Þ T
k¼1
Hence
1 6 1 ¼ rT r rT y ¼ 0:84082 4:21875 b1 0:5 ¼ 1
h¼
a1
4:21875 3:106445
2:665527
3:890625
Finally, the estimate of yð1Þ, derived using Eq. (13.2-2a), is yð1Þ ¼
1 ½b uð1Þ yð0Þ ¼ 2½1 a1 1
1 ¼ 0
13.2.2 Higher-Order Systems Here, the results of Subsec. 13.2.1 will be extended to cover the general case where the difference equation is of order n and has the form yðkÞ þ a1 yðk 1Þ þ þ an yðk nÞ ¼ b1 uðk 1Þ þ þ bn uðk nÞ
ð13:2-11Þ
with initial conditions yð1Þ; yð2Þ; . . . ; yðnÞ. In this case, the unknowns are the parameters a1 ; a2 ; . . . ; an , b1 ; b2 ; . . . ; bn and the initial conditions yð1Þ; yð2Þ; . . . ; yðnÞ. As before take N þ n measurements. For k ¼ 0; 1; . . . ; N þ n 1, the difference equation (13.2-11) yields the following equations: yð0Þ þ a1 yð1Þ þ þ an ðyðnÞ ¼ b1 uð1Þ þ þ bn ðuðnÞ yð1Þ þ a1 yð0Þ þ þ an yðn þ 1Þ ¼ b1 uð0Þ þ þ bn u n þ 1Þ .. . ð13:2-12aÞ yðn 1Þ þ a1 yðn 2Þ þ þ an yð1Þ ¼ b1 uðn 2Þ þ þ bn uð1Þ
588
Chapter 13
yðnÞ þ a1 yðn 1Þ þ þ an yð0Þ ¼ b1 uðn 1Þ þ þ bn uð0Þ yðn þ 1Þ þ a1 yðnÞ þ þ an yð1Þ ¼ b1 uðnÞ þ þ bn uð1Þ .. .
ð13:2-12bÞ
yðn þ N 1Þ þ a1 yðn þ N 2Þ þ þ an yðN 1Þ ¼ b1 uðn þ N 2Þ þ
þ bn uðN 1Þ
Relation (13.2-12) has a total of N þ n algebraic equations: the first n equations are in Eqs (13.2-12a), whereas the remaining N equations are in Eqs (13.2-12b). Define: hT ¼ ½ a1
a2
an
y ¼ ½ yðnÞ yðn þ 1Þ 3 2 uT ð0Þ 7 6 6 7 7 6 6 uT ð1Þ 7 7 6 7 6 7 r¼6 7 6 7 6 .. 7 6 . 7 6 7 6 4 5 T
b1
b2
bn ;
ð13:2-13aÞ
yðn þ N 1Þ
uT ðN 1Þ 2 yðn 1Þ 6 6 6 6 yðnÞ 6 6 ¼6 6 6 6 6 4 yðn þ N 2Þ
yð0Þ yð1Þ
uðn 1Þ uðnÞ
.. .
.. . yðN 1Þ
uðn þ N 2Þ
uð0Þ uð1Þ
3 7 7 7 7 7 7 7 7 7 7 7 5
uðN 1Þ ð13:2-13bÞ
Using the foregoing definitions, Eqs (13.2-12b) can be written compactly as follows: y ¼ rh
ð13:2-14Þ
Based on the relation (13.2-14), the results derived for the first-order systems of Subsec. 13.2.1 can easily be extended to the higher-order case. Hence, the canonical equation for Eq. (13.2-14) takes the form rT rh ¼ rT y and therefore 1 h ¼ rT r rT y ¼ r# y under the assumption that the matrix rT r is invertible.
ð13:2-15Þ
ð13:2-16Þ
System Identification
589
Example 13.2.2 Consider a discrete-time system described by the following second-order difference equation: yðk þ 2Þ þ !2 yðkÞ ¼ buðkÞ The input uðkÞ and the output yðkÞ, for N ¼ 5, are presented in Table 13.2. Estimate the parameters ! and b. Solution For k ¼ 0, 1, 2, 3, we have yð2Þ þ !2 yð0Þ ¼ buð0Þ þ eð0Þ yð3Þ þ !2 yð1Þ ¼ buð1Þ þ eð1Þ yð4Þ þ !2 yð2Þ ¼ buð2Þ þ eð2Þ yð5Þ þ !2 yð3Þ ¼ buð3Þ þ eð3Þ where eðkÞ is the measurement error at time k. The above equations can be grouped as follows: y þ rh þ e where yT ¼ ½ yð2Þ yð3Þ yð4Þ yð5Þ ¼ ½ 1=2 1=3 1=4 1=5 3 3 2 3 2 2 0 1 yð0Þ 1 eð2Þ " # 6 yð1Þ 1 7 6 0 6 eð3Þ 7 17 !2 7 7 6 7 6 6 h¼ r¼6 ; e¼6 7; 7 7¼6 4 yð2Þ 1 5 4 1=2 1 5 4 eð4Þ 5 b 1=3 1 yð3Þ 1 eð5Þ Using the above, we have 13=36 5=6 23=120 T T r r¼ ; r y¼ ; 5=6 4 77=60 T 1 36 4 5=6 ¼ r r 27 5=6 13:36 The optimum estimates of !2 and b are obtained from 2 T 1 T 0:404 ! h¼ ¼ r r r y¼ 0:405 b whereupon ! ¼ ð0:404Þ1=2 ¼ 0:635 and b ¼ 0:405. Table 13.2
Input–Output Data Sequence for Example 13.2.2
k
0
1
2
3
4
5
uðkÞ yðkÞ
1 0
1 0
1 1/2
1 1/3
1 1/4
1 1/5
590
Chapter 13
13.3
ON-LINE PARAMETER ESTIMATION
In many practical cases, it is necessary that parameter estimation takes place concurrently with the system’s operation. This parameter estimation problem is called on-line identification and its methodology usually leads to a recursive procedure for every new measurement (or data entry). For this reason, it is also called recursive identification. In simple words, on-line identification is based on the following idea. Assume that we have available an estimate of the parameter vector h based on N pairs of input–output data entries. Let this estimate be denoted by hðNÞ. Assume that hðNÞ is not accurate enough and we wish to improve the accuracy using the new (the next) N þ 1 data entry. Clearly, using N þ 1 data entries, we will obtain a new estimate for h, denoted as hðN þ 1Þ, which is expected to be an improved estimate compared with the previous estimate hðNÞ. Now, it is natural to ask the following question. For the calculation of hðN þ 1Þ, do we have to estimate hðN þ 1Þ right from the beginning, based on Eq. (13.2-16), or is there an easier way by taking advantage of the already known parameter vector hðNÞ? The answer to this question is that the estimate hðN þ 1Þ may indeed be determined in terms hðNÞ, in accordance with the following general expression: hðN þ 1Þ ¼ hðNÞ þ h
ð13:3-1Þ
where h is the change in hðNÞ because of the new N þ 1 measurement. Expression (13.3-1) is computationally attractive, since for each new measurement we do not have to compute hðN þ 1Þ from the beginning, a fact which requires a great deal of computation, but determine only the correction term h, which requires much less computation. Even though the calculation of the correction term h is not always simple, for the case of linear time-invariant systems, a computationally simple expression for h may be found, as shown below. To this end, we return to the results of Subsec. 13.2.2. Since the initial conditions yð1Þ; yð2Þ; . . . ; yðnÞ are not of interest in on-line identification, they are dropped from the identification procedure. We are therefore left with the canonical equation (13.2-14) which, for simplicity, will be stated in the rest of the chapter as follows: rðNÞhðNÞ ¼ yðNÞ Working as usual, we obtain the following estimate for hðNÞ:
1 hðNÞ ¼ rT ðNÞrðNÞ rT ðNÞyðNÞ
ð13:3-2Þ
ð13:3-3Þ
We may partition rðN þ 1Þ and yðN þ 1Þ as follows: rðNÞ yðNÞ yðNÞ rðN þ 1Þ ¼ T ¼ and yðN þ 1Þ ¼ yðN þ 1Þ yNþ1 u ðN þ 1Þ ð13:3-4Þ where yNþ1 indicates the last measurement yðN þ 1Þ in order to avoid any confusion between the vector yðN þ 1Þ and the data entry yNþ1 . Then, Eq. (13.3-2) for the N þ 1 measurements takes on the form rðN þ 1ÞhðN þ 1Þ ¼ yðN þ 1Þ Hence
ð13:3-5Þ
System Identification
591
1 hðN þ 1Þ ¼ rT ðN þ 1ÞrðN þ 1Þ rT ðN þ 1ÞyðN þ 1Þ
1 T ¼ rT ðNÞrðNÞ þ uðN þ 1ÞuT ðN þ 1Þ u ðNÞyðNÞ þ uðN þ 1ÞyNþ1 ð13:3-6Þ where use was made of Eq. (13.3-4). Equation (13.3-6) may take the form of Eq. (13.3-1) by using the following formula (known as the matrix inversion lemma):
1 ð13:3-7Þ ½A þ BCD1 ¼ A1 A1 B C1 þ DA1 B DA1 The foregoing equation can easily be verified. To this end, the matrix ½A þ BCD is multiplied from the left to the right-hand side of Eq. (13.3-7), to yield h i
1 ½A þ BCD A1 A1 B C1 þ DA1 B DA1
1 ¼ I B C1 þ DA1 B DA1 þ BCDA1
1 BCDA1 B C1 þ DA1 B DA1
1 ¼ I þ BCDA1 BC C1 þ DA1 B C1 þ DA1 B DA1 ¼ I þ BCDA1 BCDA1 ¼ I Hence, the proof is completed. Now, using Eq. (13.3-7), we obtain T
1 T
1 T
1 r ðNÞrðNÞ þ uðN þ 1ÞuT ðN þ 1Þ ¼ r ðNÞrðNÞ ¼ r ðNÞrðNÞ h i1
1 rðN þ 1Þ 1 þ uT ðN þ 1Þ rT ðNÞrðNÞ uðN þ 1Þ uT ðN þ 1Þ
1 T ð13:3-8Þ r ðNÞrðNÞ Substituting Eq. (13.3-8) in Eq. (13.3-6), we obtain h
1
hðN þ 1Þ ¼ rT ðNÞrðNÞ rT ðNÞyðNÞ þ rT ðNÞrðNÞ þ uðN þ 1ÞuT ðN þ 1Þ
1 i1 T
1 rT ðNÞrðNÞ r ðNÞyðNÞ þ rT ðNÞrðNÞ þ uðN þ 1ÞuðN þ 1Þ uðN þ 1ÞyNþ1
ð13:3-9Þ
The following holds true: h
1 T
1 i T r ðNÞyðNÞ r ðNÞrðNÞ rT ðNÞrðNÞ þ uðN þ 1ÞuT ðN þ 1Þ h T
1 ¼ r ðNÞrðNÞ þ uðN þ 1ÞuT ðN þ 1Þ rT ðNÞrðNÞ rT ðNÞrðNÞ
i
1 þ uðN þ 1ÞuT ðN þ 1Þ rT ðNÞrðNÞ rT ðNÞyðNÞ ¼ rT ðNÞrðNÞ
1
1 þ uðN þ 1ÞuT ðN þ 1Þ uðN þ 1ÞuT ðN þ 1Þ rT ðNÞrðNÞ rT ðNÞyðNÞ
1 ¼ rT ðNÞrðNÞ þ uðN þ 1ÞuT ðN þ 1Þ uðN þ 1ÞuT ðN þ 1ÞhðNÞ where, in deriving the final step in the foregoing equation, use was made of Eq. (13.3-3). Using this result, Eq. (13.3-9) can be written as
592
Chapter 13
1 hðN þ 1Þ ¼ hðNÞ rT ðNÞrðNÞ þ uðN þ 1ÞuT ðN þ 1Þ ’ðN þ 1ÞuT ðN þ 1Þ
1 hðNÞ þ rT ðNÞrðNÞ þ uðN þ 1ÞuT ðN þ 1Þ uðN þ 1ÞyNþ1 ð13:3-10Þ where use was made of Eq. (13.3-3). Finally, defining
1 cðNÞ ¼ rT ðNÞrðNÞ þ uðN þ 1ÞuT ðN þ 1Þ uðN þ 1Þ
1 ¼ rT ðN þ 1ÞrðN þ 1Þ uðN þ 1Þ
ð13:3-11Þ
Eq. (13.3-10) is transformed as follows:
hðN þ 1Þ ¼ hðNÞ þ cðNÞ yNþ1 uT ðN þ 1ÞhðNÞ
ð13:3-12Þ
Equation (13.3-12) is of the general form (13.3-1) sought. Unfortunately, the determination of the vector cðNÞ of Eq. (13.3-11) is numerically cumbersome since it requires inversion of the matrix rT ðN þ 1ÞrðN þ 1Þ at every step. To overcome this difficulty, define
1 PðNÞ ¼ rT ðNÞrðNÞ ð13:3-13Þ Hence
1 T
1 ¼ r ðNÞrðNÞ þ uðN þ 1ÞuT ðN þ 1Þ PðN þ 1Þ ¼ rT ðN þ 1ÞrðN þ 1Þ Using Eq. (13.3-8), the matrix PðN þ 1Þ can be written as
1 PðN þ 1Þ ¼ PðNÞ PðNÞuðN þ 1Þ 1 þ uT ðN þ 1ÞPðNÞuðN þ 1Þ uT ðN þ 1ÞPðNÞ
ð13:3-14Þ
Equation (13.3-14) offers a convenient way
for calculating PðN þ 1Þ. It is noted that the term 1 þ uT ðN þ 1ÞPðNÞuðN þ 1Þ is scalar, whereupon calculation of its inverse is simple. The calculation of the matrix PðN þ 1Þ, according to the recursive formula (13.3-14), requires a matrix inversion only once at the beginning of the procedure to obtain
1 PðN0 Þ ¼ rT ðN0 ÞrðN0 Þ where N0 is the starting number of data entries. Upon computing PðN þ 1Þ, the vector cðNÞ can easily be determined from Eq. (13.3-11), i.e., from the expression cðNÞ ¼ PðN þ 1ÞuðN þ 1Þ
ð13:3-15Þ
In summary, the proposed recursive algorithm is given by the following theorem. Theorem 13.3.1 Suppose that hðNÞ is the estimate of the parameters of the nth order system (13.2-11) for N data entries. Then, the estimate of the parameter vector hðN þ 1Þ for N þ 1 data entries is given by the expression
hðN þ 1Þ ¼ hðNÞ þ cðNÞ yNþ1 uT ðN þ 1ÞhðNÞ ð13:3-16Þ where cðNÞ ¼ PðN þ 1ÞuðN þ 1Þ
ð13:3-17aÞ
System Identification
593
and the matrix PðN þ 1Þ is calculated from the recursive formula
1 PðN þ 1Þ ¼ PðNÞ PðNÞuðN þ 1Þ 1 þ uT ðN þ 1ÞPðNÞuðN þ 1Þ uT ðN þ 1ÞPðNÞ with initial conditions
1 PðN0 Þ ¼ rT ðN0 ÞrðN0 Þ hðN0 Þ ¼ PðN0 ÞrT ðN0 ÞyðN0 Þ
ð13:3-17bÞ
ð13:3-17cÞ ð13:3-17dÞ
In Figure 13.1, a block diagram of the on-line algorithm is given. It is clear that at every step the (known) inputs are the yNþ1 , uT ðN þ 1Þ, hðNÞ, and PðNÞ and the algorithm produces the new estimate hðN þ 1Þ. The algorithm also produces the matrix PðN þ 1Þ, which is used in the next step. In Figure 13.2, a more detailed block diagram of the on-line algorithm is given. Remark 13.3.1 In Eq. (13.3-16) we observe that the correction term h, defined in Eq. (13.3-1), is proportional to the difference yNþ1 uT ðN þ 1ÞhðNÞ, where yNþ1 is the new data entry and uT ðN þ 1ÞhðNÞ is an estimate of this new entry, based on Eq. (13.3-5) and using the latest estimate of the system parameters. Had there not existed any error in the measurements, the expected value uT ðN þ 1ÞhðNÞ of yNþ1 would be equal to the respective measurement value yNþ1 and the difference between them would be zero, in which case hðN þ 1Þ ¼ hðNÞ. In other words, when there is no error in the data entries, a new entry does not add any new information, so the new estimate of h has exactly the same value as that of the previous estimate. Finally, it is noted that the term ðNÞ may be considered as a weighting factor of the difference term yNþ1 uT ðN þ 1ÞhðNÞ. Example 13.3.1 Consider the simple case of a resistive network given in Figure 13.3. Estimate the parameter a when uðkÞ is a step sequence, i.e., when uðkÞ ¼ 1, for k ¼ 1; 2; 3; . . . . Solution The difference equation for the system is yðkÞ ¼ auðkÞ. Since uðkÞ ¼ 1, for all k, it follows that
Figure 13.1
Block diagram presentation of the on-line algorithm.
594
Chapter 13
Figure 13.2
Detailed block diagram representation of the on-line algorithm.
yðkÞ ¼ a þ eðkÞ;
k ¼ 1; 2; . . . ; N
To start with, we solve the problem using the following very simple technique. Define the cost function J¼
N X k¼1
e2 ðkÞ ¼
N X ½yðkÞ a2 k¼1
Then N X @J ¼ 2 ½yðkÞ a ¼ 0 @a k¼1
The foregoing equation can be written as " # N N X X ½yðkÞ a ¼ yðkÞ Na ¼ 0 k¼1
k¼1
Hence, the estimate aðNÞ of the parameter a is aðNÞ ¼
N 1X yðkÞ N k¼1
The expression above for aðNÞ is the mean value of the measurements yðkÞ, as was anticipated. Assume now that we have a new measurement. Then
Figure 13.3
Simple case of a resistive network.
System Identification
aðN þ 1Þ ¼
595
þ1 X 1 N yðkÞ N þ 1 k¼1
¼
N N N 1X 1X 1 X yðkÞ yðkÞ þ ½yðkÞ þ yðN þ 1Þ N k¼1 N k¼1 N þ 1 k¼1
¼
N N 1X N ðN þ 1Þ X 1 yðN þ 1Þ yðkÞ þ yðkÞ þ N k¼1 NðN þ 1Þ k¼1 Nþ1
¼ aðNÞ þ
1 ½yðN þ 1Þ aðNÞ N þ1
The foregoing equation represents the recursive algorithm (13.3-16), where uT ðN þ 1Þ ¼ 1, yNþ1 ¼ yðN þ 1Þ, and cðNÞ ¼ 1=ðN þ 1Þ. Now, we solve the problem using the method presented in this section. As a first step, we formulate the canonical equation rðNÞhðNÞ ¼ yðNÞ where hðNÞ ¼ aðNÞ, rðNÞ ¼ ½1 1 1T , and yðNÞ ¼ ½yð1Þ yð2Þ The solution of the canonical equation is
yðNÞT .
N X
1 yðkÞ aðNÞ ¼ rT ðNÞrðNÞ rT ðNÞyðNÞ ¼ N 1 rT ðNÞyðNÞ ¼ N 1 k¼1
We observe that PðNÞ ¼ N 1 . Thus Eq. (13.3-17b) yields
PðN þ 1Þ ¼ N 1 N 1 1 þ N 1 N 1 ¼
1 Nþ1
whereas Eq. (13.3-17a) gives cðNÞ ¼ PðN þ 1ÞuðN þ 1Þ ¼
1 Nþ1
Hence, Eq. (13.3-16) becomes aðN þ 1Þ ¼ aðNÞ þ
1 y aðNÞ N þ 1 Nþ1
Figure 13.4 shows the block diagram of the ON-LINE algorithm for Example 13.3.1. Example 13.3.2 Consider the system of Example 13.3.1 with the difference that the input is not a unit step function but any other type of function. Estimate the parameter a. Solution The difference equation is yðkÞ ¼ auðkÞ. We formulate the canonical equation rðNÞhðNÞ ¼ yðNÞ where
596
Chapter 13
Figure 13.4
Block diagram of the on-line algorithm of Example 13.3.1.
hðNÞ ¼ aðNÞ;
rðNÞ ¼ ½uð1Þ uð2Þ
yðNÞ ¼ ½yð1Þ yð2Þ
uðNÞT
and
T
yðNÞ
The solution of the canonical equation gives the following result: " #1 N N X X
1 T T 2 u ðkÞ uðkÞ’ðkÞ aðNÞ ¼ r ðNÞrðNÞ r ðNÞyðNÞ ¼ k¼1
k¼1
We observe that " #1 N X 2 u ðkÞ PðNÞ ¼ k¼1
Hence, Eq. (13.3-17b) becomes PðN þ 1Þ ¼ PðNÞ PðNÞuðN þ 1Þ½1 þ u2 ðN þ 1ÞPðNÞ1 uðN þ 1ÞPðNÞ
¼ ½1 þ u2 ðN þ 1ÞPðNÞ u2 ðN þ 1ÞPðNÞ PðNÞ½1 þ u2 ðN þ 1ÞPðNÞ1 ¼ PðNÞ½1 þ u2 ðN þ 1ÞPðNÞ1 Therefore
aðN þ 1Þ ¼ aðNÞ þ cðNÞ yNþ1 uT ðN þ 1ÞaðNÞ
¼ aðNÞ þ PðNÞuðN þ 1Þ½1 þ u2 ðN þ 1ÞPðNÞ1 yNþ1 uðN þ 1ÞaðNÞ
If uðkÞ ¼ 1, k ¼ 1; 2; . . . ; the results of Example 13.3.1 can readily be derived as a special case of the foregoing results. Indeed, since PðNÞ ¼ N 1 and uðkÞ ¼ 1, k ¼ 1; 2; . . . ; the above expression for aðN þ 1Þ becomes
aðN þ 1Þ ¼ aðNÞ þ N 1 1 þ N 1 yNþ1 aðNÞ
1 ¼ aðNÞ þ yNþ1 aðNÞ Nþ1 Example 13.3.3 Consider the discrete-time system described by the nonlinear difference equation yðk þ 1Þ þ ay2 ðkÞ ¼ buðkÞ
System Identification
597
If the unit uðkÞ is a unit step sequence, it results in the following output measurements: k
0
1
2
3
4
5
yðkÞ
0
0.01
1.05
1.69
3.02
7.4
6 39.3
Determine: (a) An estimate of the parameters a and b. (b) Assume that a new output measurement yð7Þ ¼ 1082 is available. Find the new estimates for a and b using the recursive formula. Solution (a) For k ¼ 1, 2, 3, 4, 5 we have yð2Þ þ ay2 ð1Þ ¼ buð1Þ þ eð2Þ yð3Þ þ ay2 ð2Þ ¼ buð2Þ þ eð3Þ yð4Þ þ ay2 ð3Þ ¼ buð3Þ þ eð4Þ yð5Þ þ ay2 ð4Þ ¼ buð4Þ þ eð5Þ yð6Þ þ ay2 ð5Þ ¼ buð5Þ þ eð6Þ where eðkÞ is the measurement error at time k. The above equations can be written compactly as follows: yðNÞ ¼ rðNÞhðNÞ þ e where yT ðNÞ ¼ ½ yð2Þ yð3Þ yð6Þ ¼ ½ 1:05 1:69 3 2 2 3 2 0:0001 1 y ð1Þ uð1Þ 7 6 2 7 6 6 y ð2Þ uð2Þ 7 6 1:1025 1 7 7 6 2 7 6 7 7 6 rðNÞ ¼ 6 6 y ð3Þ uð3Þ 7 ¼ 6 2:8561 1 7; 7 6 2 7 6 4 y ð4Þ uð4Þ 5 4 9:1204 1 5 y2 ð5Þ uð5Þ 3 eð2Þ 7 6 6 eð3Þ 7 7 6 7 e¼6 6 eð4Þ 7 7 6 4 eð5Þ 5 eð6Þ 2
Using the above results, we obtain
54:76
1
3:02
7:4
hðNÞ ¼
39:3
a ; b
598
Chapter 13
rT ðNÞrðNÞ ¼
1 rT ðNÞrðNÞ
3091:12 67:839 ; 67:839 5 0:0005 0:0063 ¼ 0:0063 0:2848
rT ðNÞyðNÞ ¼
2230:04771 52:46
The optimal estimate of the parameters a and b is
1 a 0:7845 ¼ rT ðNÞrðNÞ rT ðNÞyðNÞ ¼ hðNÞ ¼ 0:891 b (b) Using the recursive equation (13.3-16), we have
hð7Þ ¼ hð6Þ þ cð6Þ y7 uT ð7Þhð6Þ where
0:7845 hð6Þ ¼ ; y7 ¼ yð7Þ ¼ 1082; 0:891 0:0006 cð6Þ ¼ Pð7Þuð7Þ ¼ 0:008
uT ð7Þ ¼ ½1544:5
1
Hence a 0:7062 hð7Þ ¼ ¼ b 1:9354
13.4
PROBLEMS
1. A system is described by the following difference equation yðk þ 3Þ þ a1 yðk þ 2Þ þ a2 yðk þ 1Þ þ a3 yðkÞ ¼ uðkÞ If the input to the system is the impulse function uðkÞ ¼ ðkÞ, it results in the following output measurements: k
0
1
yðkÞ
1
0.2
2
3
4
5
6
0:6
1:2
1:6
1:7
1:6
Estimate the parameters a1 , a2 , and a3 : 2. Estimate the parameters a, b, and ! of a system described by the difference equation yðkÞ þ !2 yðk 2Þ ¼ auðk 1Þ þ buðk 2Þ given that the following measurements are available: k
1
2
3
4
5
uðkÞ yðkÞ
1 0
1 1/2
1 1/3
1 1/4
1 1/5
System Identification
599
Are the estimates unique? Explain your results. 3. Estimate the parameters a, b, and c of the system yðkÞ þ ayðk 1Þ ¼ bu2 ðkÞ þ cuðk 1Þ given the information shown in Table 13.3 regarding its input and output. Make use of all the information provided in the table. The resulting identification must be unique. 4. Let a system be described by yðk þ 2Þ ¼ ayðk þ 1Þ þ yðkÞ þ buðkÞ (a)
(b)
Estimate the parameters a and b given the following measurements: k
0
1
2
3
4
uðkÞ yðkÞ
1 1
1 0.9
1 0.9
1 0.8
1 0.6
What are the new parameter estimates in view of the additional measurements uð5Þ ¼ 1 and yð5Þ ¼ 0:4?
5. The output of a given system HðzÞ is compared with the output of a known system H2 ðzÞ, as shown in Figure 13.5. It is known that the difference equation that describes HðzÞ is of the form yðkÞ ¼ auðkÞ þ buðk 1Þ þ cuðk 2Þ The equation of H2 ðzÞ is yðkÞ ¼ uðkÞ þ 2uðk 1Þ 3uðk 2Þ and the feedback coefficient f ¼ 1. Find the unknown parameters a, b, and c given the following measurements:
k
0
1
2
3
4
5
rðkÞ dðkÞ
1 0
1 0
1 0:5
1 0:8
1 1:1
1 1:4
Table 13.3
Output Sequences yðkÞ of Several Inputs for
Problem 3 Input u1 ðkÞ ¼ k2 u2 ðkÞ ¼ k yðkÞ ¼ 1
yð1Þ
yð2Þ
yð3Þ
yð4Þ
0 0 0
0 0:5 0:5
1 1 1
1.5 2.5 4
600
Chapter 13
Figure 13.5
Closed-loop configuration.
REFERENCES Books 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.
KJ Astrom, B Wittenmark. Computer Controlled Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1984. CI Byrnes, A Lindquist (eds). Modelling, Identification and Robust Control. Amsterdam: North Holland, 1986. R Calaba, K Spingarn. Control, Identification and Input Optimization. New York: Plenum Press, 1982. RH Cannon Jr. Dynamics of Physical Systems. New York: McGraw-Hill, 1967. P Eykhoff. System Identification. Parameter and State Estimation. New York: John Wiley & Sons, 1977. P Eykhoff. Trends and Progress in System Identification. Oxford: Pergamon Press, 1981. GC Goodwin, RL Payne. Dynamic System Identification: Experimental Design and Data Analysis. New York: Academic Press, 1977. TC Hsia. System Identification. Boston, MA: Lexington, 1977. L Ljung, T So¨destro¨m. Theory and Practice of Recursive Identification. Cambridge, MA: MIT Press, 1983. L Ljung. System Identification-Theory for the User. Englewood Cliffs, New Jersey: Prentice Hall, 1987. RK Mehra, DG Lainiotis (eds). System Identification. Advances and Case Studies. New York: Academic Press, 1976. JM Mendel. Discrete Techniques of Parameter Estimation. New York: Marcel Dekker, 1973. M Milanese, R Tempo, A Vicino (eds). Robustness in Identification and Control. New York: Plenum Press, 1989. JP Norton. An Introduction to Identification. London: Academic press, 1986. PN Paraskevopoulos. Digital Control Systems. London: Prentice Hall, 1996. MS Rajbman, VM Chadeer. Identification of Industrial Processes. Amsterdam: North Holland, 1980. AD Sage, JL Melsa. System Identification. New York: Academic Press, 1971. T So¨derstro¨m, P Stoica. System Identification. London: Prentice Hall, 1989. H W Sorenson. Parameter Estimation. Principles and Problems. New York: Marcel Dekker, 1980. CB Speedy, RF Brown, GC Goodwin. Control Theory: Identification and Optimal Control. Edinburgh: Oliver and Boyd, 1970.
System Identification 21.
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E Walter. Identification of State Space Models. Berlin: Springer Verlag, 1982.
Articles 22. 23.
KJ Astrom, PE Eykhoff. System identification—a survey. Automatica 7:123–162, 1971. Special issue on identification and system parameter estimation. Automatica 17, 1981.
14 Adaptive Control
14.1
INTRODUCTION
An adaptive control system is a system which adjusts automatically on-line the parameters of its controller, so as to maintain a satisfactory level of performance when the parameters of the system under control are unknown and/or time varying. Generally speaking, the performance of a system is affected either by external perturbations or by parameter variations. Closed-loop systems involving feedback (top portion of Figure 14.1), are used to cope with external perturbations. In this case, the measured value of the output yðkTÞ is compared with the desired value of the reference signal rðkTÞ. The difference eðkT Þ between the two signals is applied to the controller, which in turn provides the appropriate control action uðkTÞ to the plant or system under control. A somewhat similar approach can be used when parametric uncertainties (unknown parameters) appear in the system model of Figure 14.1. In this case the controller involves adjustable parameters. A performance index is defined, reflecting the actual performance of the system. This index is then measured and compared with a desired performance index (see Figure 14.1) and the error between the two performance indices activates the controller adaptation mechanism. This mechanism is suitably designed so as to adjust the parameters of the controller (or modify the input signals in a more general case), so that the error between the two performance indices lies within acceptable bounds. Closer examination of Figure 14.1 reveals that two closed loops are involved: the ‘‘inner’’ feedback closed loop, whose controller involves adjustable parameters (upper portion of the figure); the supplementary ‘‘outer’’ feedback closed loop (or adaptation loop), which involves the performance indices and the adaptation mechanism (lower portion of the figure). The role of the adaptation loop is to find appropriate estimates for the adjustable controller parameters at each sampling instant. It should be mentioned that a general definition, on the basis of which one could characterize a system as being adaptive or not, is still missing. However, it is clear that constant feedback systems are not adaptive systems. The existence of a feedback loop involving the performance index of the closed-loop system is a safe rule for characterizing a system as adaptive or not. 603
604
Figure 14.1
Chapter 14
Block diagram of a general adaptive control system.
An adaptive control system is inherently nonlinear, since the controller parameters are nonlinear functions of the measured signals through the adaptation mechanism. This is true even for the control of linear systems with unknown parameters, a fact which makes the analysis of adaptive systems very difficult. This analysis involves the stability characteristics of the closed-loop system, the satisfaction of the performance requirements, and the convergence of the parameter estimates. Adaptive control has been under investigation for many years. Major breakthroughs in the area have been reported in the last two decades [1–27]. Adaptive control schemes have been applied in the paper industries, rolling mills, power plants, motor drives, chemical reactors, cement mills, autopilots for aircrafts, missiles and ships, etc. Microprocessor advances have made it quite easy to implement adaptive controllers and at low cost. The use of adaptive controllers may lead to improvement of product quality, increase in production rates, fault detection, and energy saving. The two basic techniques to control discrete-time systems with unknown parameters are the model reference adaptive control (MRAC) scheme [8, 20–24] and selftuning regulators (STRs) [2, 7, 15–18, 26, 27]. These two techniques are presented in Secs 14.3 and 14.4, respectively, and constitute a condensed version of the material reported in Chap. 9 in [12]. In MRAC, a reference model is used explicitly in the control scheme and sets the desired performance. Then, an appropriate on-line adaptation mechanism is designed to adjust the controller parameters at each step, so that the output of the system converges to the output of the reference model asymptotically, while simul-
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taneously the stability of the closed-loop system is secured. In STRs, the control design and the adaptation procedure are separate. Different parameter estimators can be combined with appropriate control schemes to yield a variety of STRs. Restrictions in the structure of the models under which both methods can be applied are discussed on several appropriate occasions in the material of this chapter. Model reference adaptive controllers can be either direct or indirect. The essential difference between them is that in direct MRAC the controller parameters are directly adjusted by the adaptation mechanism, while in indirect MRAC the adjustment of the controller parameters is made in two steps. In the first step, the control law is reparametrized so that the plant parameters appear explicitly in the control law. A relation between the controller parameters and the plant parameters is thus established. The plant parameters are adjusted by the adaptation mechanism. In the second step, the controller parameters are calculated from the estimates of the plant parameters. Direct MRAC, using the hyperstability approach for proving stability, is discussed in Sec. 14.3. STRs can be either explicit or implicit. In explicit STRs an estimate of the explicit plant-model parameters is obtained. The explicit plant model is the actual plant model. In implicit STRs the parameters of an implicit model are estimated. The implicit model is a reparametrization of the explicit plant model. The parameters of the implicit model and those of the controller are the same; therefore, we call the plant parameters explicit or indirect and the controller parameters implicit or direct. Though of different nature and origin, a close relation between MRAC systems and STRS has been established [18, 20]. It is clear that explicit self-tuners correspond to indirect MRAC schemes, while implicit self-tuners correspond to direct MRAC schemes. Self-tuners based on pole-placement control are presented in Sec. 14.4.2. Another approach to discrete-time MRAC is that of using Lyapunov functions to prove asymptotic stability and the satisfaction of performance requirements. An expression for the error between the output of the reference model and that of the plant is formed and then the adaptation mechanism is chosen in order to make the increments of a Lyapunov candidate function negative. This method is not developed in this chapter. A demonstration by using a simple example, can be found in [12]. The difficulty of finding an appropriate Lyapunov candidate function in the general discrete-time case restricts the use of this method. The hyperstability approach of Sec. 14.3 is preferable for discrete-time MRAC systems, while for continuous-time systems the Lyapunov design has mainly been used. A first approach to MRAC was based on the gradient method. The parameter adaptation scheme obtained for synthesizing the adaptive loop was heuristically developed, initially for continuous-time systems and is known as the MIT rule [3]. A version of MRAC for discrete-time systems, based on the gradient method, is presented below. 14.2
ADAPTIVE CONTROL WITH THE GRADIENT METHOD (MIT RULE)
Consider a system with a single output yðkT; hÞ, where T is the sampling period and h is the vector of unknown parameters which parametrizes the adjustable controller (hence the system’s input signal is a function of h, i.e., uðkT; hÞ) and the output of the system. The control objective is to follow the output ym ðkTÞ of a reference model, in
606
Chapter 14
the sense that a particular performance eðkT; hÞ ¼ yðkT; hÞ ym ðkTÞ, is a minimum. Consider the quadratice performance index
index,
involving
JðkT ; hÞ ¼ 12 e2 ðkT; hÞ
the
error
ð14:2-1Þ
It is obvious that to minimize the JðkT; hÞ, the parameter vector h of the adjustable controller should change in the opposite direction to that of the gradient @J=@h. Consequently, the adaptation rule for h, i.e., the difference equation giving the time evolution of h at the sampling instants, is @JðkT; hÞ @eðkT; h ¼ hðkTÞ eðkT; hÞ ð14:2-2Þ hðkT þ TÞ ¼ hðkTÞ @h @h where is a constant positive adaptation gain. More precisely, when @JðkT; hÞ=@h is negative, i.e., when J decreases while h increases, then h should increase in order for J to decrease further. In the case where @JðkT; hÞ=@h is positive, i.e., when J and h increase simultaneously, then h should decrease in order for J to decrease further. This is achieved by the heuristic adaptation mechanism of Eq. (14.2-2). The partial derivative @eðkT; hÞ=@h appearing in Eq. (14.2-2) is called the system’s sensitivity derivative. For the ‘‘MIT rule’’ to perform well, the adaptation gain should be small, since its value influences the convergence rate significantly. Moreover, it is possible for the ‘‘MIT rule’’ to lead to an unstable closed-loop system, since it is only a heuristic algorithm not rigidly based on stability requirements. Other performance indices are also possible. The following example will illustrate the application of the MIT rule. This example will reveal the main problem in applying this method: namely, the necessity of using approximations to calculate the sensitivity derivatives of a certain system. Example 14.2.1 Consider a first-order system described by the difference equation yðkT þ TÞ ¼ ayðkTÞ þ buðkTÞ
ð14:2-3Þ
where uðkTÞ is the input and yðkTÞ is the output. It is desired to obtain a closed-loop system of the form ym ðkT þ TÞ ¼ am ym ðkT Þ þ bm rðkTÞ
ð14:2-4Þ
where rðkTÞ is a bounded reference sequence and ym ðkTÞ is the output of the reference model. To this end, an output feedback control law is used, having the form uðkTÞ ¼ fyðkTÞ þ grðkTÞ
ð14:2-5Þ
Assume that the system parameters a and b are unknown. Determine the appropriate adaptation mechanism for the controller parameters f and g, using the MIT rule. Solution Combining Eqs (14.2-3) and (14.2-5), we obtain the closed-loop system yðkT þ TÞ ¼ ayðkTÞ þ b½fyðkTÞ þ grðkTÞ ¼ ða þ bf ÞyðkTÞ þ bgrðkTÞ or yðkTÞ ¼ ða þ bf ÞyðkT TÞ þ bgrðkT TÞ
Adaptive Control
or
# bgq1 yðkTÞ ¼ rðkT Þ 1 þ ða þ bf Þq1
607
"
ð14:2-6Þ
where q1 is the backward shift operator such that q1 yðkTÞ yðkT TÞ. Comparing Eqs (14.2-4) and (14.2-6), we have that in the case of known parameters a and b, the particular choice f ¼ ðam aÞ=b and g ¼ bm =b leads to satisfaction of the control objective. This case is called perfect model following. In the case of uncertain system parameters, we will use the ‘‘MIT rule.’’ Here, the controller is parametrized by the adjustable parameters f ðkÞ and gðkÞ. The error eðkTÞ between the outputs of the system and the reference model is now given by " # bgq1 ð14:2-7Þ eðkTÞ ¼ yðkTÞ ym ðkTÞ ¼ rðkTÞ ym ðkT Þ 1 þ ða þ bf Þq1 Using the foregoing expression for eðkTÞ, the system’s sensitivity derivatives @e=@g and @e=@f can be easily determined. We have " # @eðkTÞ bq1 ¼ rðkTÞ ð14:2-8Þ @g 1 þ ða þ bf Þq1 " # " # @eðkTÞ b2 gq2 bq1 ¼ rðkTÞ ¼ yðkTÞ ð14:2-9Þ @f 1 þ ða þ bf Þq1 ½1 þ ða þ bf Þq1 2 These expressions for the sensitivity derivatives cannot be used in the adaptation mechanism, since the unknown parameters a and b appear explicitly. For the present system, when perfect model following is achieved, we have that a þ bf ¼ am . Taking advantage of this fact, the following approximate forms can be used for the sensitivity derivatives (still containing the unknown b): " # @eðkTÞ bq1 ffi rðkT Þ ð14:2-10Þ @g 1 þ am q1 " # @eðkTÞ bq1 ffi yðkTÞ ð14:2-11Þ @f 1 þ am q1 These sensitivity derivatives lead to the following parameter adaptation laws (‘‘MIT rule’’): " # q1 gðkT þ TÞ ¼ gðkTÞ rðkTÞ eðkTÞ ð14:2-12Þ 1 þ am q1 " # q1 yðkT Þ eðkTÞ ð14:2-13Þ f ðkT þ TÞ ¼ f ðkTÞ þ 1 þ am q1 Notice here that the adaptation laws were obtained by absorbing the parameter b in the adaptation gain . This is done because b is unknown and should not appear in the adaptation laws; however, this requires that the sign of b is known. Then the sign of depends on the sign of b. The foregoing laws are initialized with arbitrary gð0Þ
608
Chapter 14
and f ð0Þ, which should reflect our a priori knowledge on the appropriate controller parameters which achieve model following. Finally, the adjustable controller is given by uðkTÞ ¼ f ðkTÞyðkTÞ þ gðkTÞrðkT Þ
ð14:2-14Þ
Equations (14.2-12), (14.2-13), and (14.2-14) specify the dynamic adaptive controller being sought. The aformentioned results can be generalized to the case of a single-input– single-output (SISO) linear system described by the difference equation Aðq1 ÞyðkTÞ ¼ qd Bðq1 uðkTÞ ð14:2-15Þ where d 1 is the system’s delay and Aðq1 Þ and Bðq1 Þ are polynomials in the backward shift operator having the form ð14:2-16Þ Aðq1 ¼ 1 þ a1 q1 þ þ anA qnA Bðq1 Þ ¼ b0 þ b1 q1 þ þ bnB qnB
ð14:2-17Þ
For more details, see [12], where both direct and indirect algorithms are developed. 14.3 14.3.1
MODEL REFERENCE ADAPTIVE CONTROL— HYPERSTABILITY DESIGN Introduction
MRAC is a systematic method for controlling plants with unknown parameters. The basic scheme of an MRAC system is presented in Figure 14.2. In comparison to the general structure of an adaptive control system given in Figure 14.1, here the desired performance index is generated by means of a reference model. The reference model is a dynamic system whose behavior is considered to be the desired (ideal) one and it is a part of the control system itself, since it appears explicitly in the control scheme. The output ym ðkTÞ of the reference model indicates how the output yðkTÞ of the plant should behave. Both systems are excited by the same command signal rðkTÞ. Comparing Figures 14.1 and 14.2, we observe that the desired performance index is now replaced by ym ðkTÞ and the measured performance index by yðkTÞ. We distinguish two control loops: the ‘‘inner’’ loop and the ‘‘outer’’ loop. The ‘‘inner’’ loop consists of the plant which involves unknown parameters and the adjustable controller. The ‘‘outer’’ loop is designed appropriately to adjust the controller’s parameters so that the error eðkT Þ ¼ yðkTÞ ym ðkTÞ approaches zero asymptotically, while the stability of the overall system can be proved using the so-called hyperstability approach. Compared with techniques which involve other kinds of performance indices, the MRAC technique is characterized by high speed of adaptation. This is because a simple subtracter is needed to form the error eðkTÞ ¼ yðkTÞ ym ðkTÞ. This error, together with other available on-line data, is then fed to the adaptation mechanism. The parameters of the adjustable controller are modified accordingly, in order to minimize the difference between the two performance indices: namely, the desired performance index and the measured performance index.
Adaptive Control
Figure 14.2
609
Block diagram of the model reference adaptive control (MRAC) scheme.
14.3.2 Definition of the Model Reference Control Problem Consider a deterministic, SISO, discrete-time, linear, time-invariant systems, described by Aðq1 Þyðk þ dÞ ¼ Bðq1 ÞuðkÞ
or
Aðq1 ÞyðkÞ ¼ qd Bðq1 ÞuðkÞ
ð14:3-1Þ
with initial condition yð0Þ 6¼ 0. Here Aðq1 Þ ¼ 1 þ a1 q1 þ þ anA qnA ¼ 1 þ q1 A ðq1 Þ Bðq1 ¼ b0 þ b1 q1 þ þ bnB qnB ¼ b0 þ q1 B ðq1 Þ;
ð14:3-2Þ b0 6¼ 0
ð14:3-3Þ
where q1 is the backward shift operator, d > 0 represents the system’s time delay, and uðkÞ and yðkÞ represent the system’s input and output signals, respectively. The following three assumptions are made for the system under control: 1.
2. 3.
The roots of Bðz1 Þ, which are the system’s zeros, are all inside the unit circle jzj < 1, i.e., zni B Bðz1 i Þ ¼ 0 with jzi j < 1. Thus, the system zeros are stable and can be canceled out without leading to an unbounded control signal. The system’s delay d is known (this implies b0 6¼ 0). An upper limit for the orders nA and nB of the polynomials Aðq1 Þ and Bðq1 Þ, respectively, is given.
610
Chapter 14
According to the foregoing assumptions, any change in the system’s characteristics should not affect the delay d, whereas the system’s zeros can move only inside the unit circle. The method is therefore valid only for minimum-phase systems. The control objective is twofold: linear model following during tracking and elimination of any initial output disturbance during regulation. It is desirable to be able to specify the tracking and regulation objectives independently. This flexibility is crucial for certain applications. The control objectives are specified as follows. 1 Tracking During tracking, it is desired for the plant output yðkÞ to satisfy the equation Am ðq1 ÞyðkÞ ¼ qd Bm ðq1 ÞrðkÞ
ð14:3-4Þ
where 1 nAm Am ðq1 Þ ¼ 1 þ am þ þ am 1q nAm q 1
Bm ðq Þ ¼
bm 0
þ
1 bm 1q
þ þ
ð14:3-5Þ
nBm bm nBm q
ð14:3-6Þ
Here, rðkÞ is a bounded reference sequence and the polynomial Am ðq1 Þ is chosen to be asymptotically stable. 2 Regulation ðr ðk Þ 0; ym ðk Þ 0Þ In regulation, the influence of any initial nonzero output yð0Þ 6¼ 0 (which corresponds to an impulse perturbation), should be eliminated via the dynamics defined by ðq1 Þyðk þ dÞ ¼ 0
for
k0
ð14:3-7Þ
1
where ðq Þ is an asymptotically stable polynomial of the designer’s choice, having the form ðq1 Þ ¼ 1 þ 1 q1 þ þ n qn
ð14:3-8Þ
Consider the following explicit reference model: Am ðq1 Þym ðkÞ ¼ qd Bm ðq1 ÞrðkÞ
ð14:3-9Þ
with input rðkÞ and output ym ðkÞ. Note here that the sequence ym ðkÞ, apart from being calculated by means of the reference model (14.3-9), can also be a predefined sequence stored in memory. It is obvious that both control objectives (i.e., tracking and regulation) can be accomplished if the control law uðkÞ is such that eðk þ dÞ ¼ ðq1 Þeðk þ dÞ ¼ ðq1 Þ½yðk þ dÞ ym ðk þ dÞ 0
for
k0 ð14:3-10Þ
The error eðkÞ is the difference between the plant and reference model outputs (plantmodel error), i.e., eðkÞ ¼ yðkÞ ym ðkÞ 1
ð14:3-11Þ
and eðkÞ ¼ ðq ÞeðkÞ is the so-called filtered error between the plant and the reference model outputs. The error eðkÞ is also called the a priori adaptation error. The foregoing objectives will be satisfied below in the case of known or unknown para-
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meters in the polynomials Aðq1 Þ and Bðq1 Þ, in Subsecs 9.3.3 and 9.3.4, respectively, by seeking appropriate control laws. If Eq. (14.3-10) is satisfied, then any initial plant-model error or any initial output disturbance, will converge to zero, i.e., limk!þ1 eðkÞ ¼ 0. Note that if ðq1 Þ ¼ 1, one has eðk þ dÞ 0, k 0, which means that in this case the plantmodel error vanishes d steps after the control input is applied. The polynomial ðq1 Þ is a filtering polynomial. As is made clear in the sequel, adaptive control performance depends critically on the choice of ðq1 Þ. 14.3.3 Design in the Case of Known Parameters In this subsection we assume that the plant parameters appearing in the polynomials Aðq1 Þ and Bðq1 Þ are known. Consider the general case where d > 1. We wish to obtain a control law uðkÞ satisfying the control objectives and being causal, i.e., not depending on future values of the input and output. This control law we seek will therefore have the form uðkÞ ¼ f ðyðkÞ; yðk 1Þ; . . . ; uðk 1Þ; uðk 2Þ; . . .Þ To this end, consider the following equation, which is equivalent to Eq. (14.3-1): yðk þ dÞ ¼ A ðq1 Þyðk þ d 1Þ þ Bðq1 ÞuðkÞ
ð14:3-12Þ
Next, we want to express Eq. (14.3-12) in the form ðq1 Þyðk þ dÞ ¼ gðyðkÞ; yðk 1Þ; . . . ; uðkÞ; uðk 1Þ; . . .Þ
ð14:3-13Þ
The specific form of g sought can be determined in two ways: either by repeatedly substituting yðk þ d 1Þ; . . . ; yðk þ 1Þ in Eq. (14.3-12) generated by the same equation (Eq. (14.3-12)) delayed in time, or more easily, by directly considering the following polynomial identity (decomposition of ðq1 ÞÞ: ðq1 Þ ¼ Aðq1 ÞSðq1 Þ þ qd Rðq1 Þ
ð14:3-14Þ
with Rðq1 Þ and Sðq1 Þ appropriate polynomials. We adopt the second method for simplicity and, to this end, the results of the following remark will be useful. Remark 14.3.1 The above identity (14.3-14) is a special case of what is referred to as the Diophantine equation or the Bezout identity. It can be proven that ðq1 Þ can be uniquely factorized as in Eq. (14.3-14), where Sðq1 Þ ¼ 1 þ s1 q1 þ þ snS qnS
ð14:3-15Þ
Rðq1 ¼ r0 þ r1 q1 þ þ rnR qnR
ð14:3-16Þ
with nS ¼ d 1 and nR ¼ maxðnA 1; n dÞ (see Subsec. 14.4.2 that follows for the uniqueness conditions). The coefficients of the polynomials Sðq1 Þ and Rðq1 Þ are uniquely determined by the solution of the following algebraic equation:
612
Chapter 14
2
32
1
6 a 6 1 6 6 a2 6 6 . 6 .. 6 6 6 ad1 6 6 a 6 d 6 6 adþ1 6 6a 6 dþ2 6 6 .. 6 . 6 6 6 6 6 4
1 1
a1
0 ad2
a1
1
ad1 ad
a2 a3
a1 a2
1 0
1
adþ1 .. .
a4 .. .
a3 .. .
0
0
1
0 .. .
0 .. .
0 .. .
0 0
0 0
0 0
1 .. . 1 0
1
3
2
1
3
76 7 7 6 76 s1 7 6 1 7 76 7 7 6 76 s2 7 6 2 7 76 7 7 6 76 . 7 6 . 7 76 . 7 6 .. 7 76 . 7 6 7 76 7 7 6 76 sd1 7 6 d1 7 76 7 7 6 76 7 7 6 76 r0 7 6 ... 7 76 7 7¼6 76 r 7 6 7 76 1 7 6 7 76 7 7 6 76 r2 7 6 7 76 7 7 6 76 . 7 6 . 7 76 .. 7 6 . 7 76 7 6 . 7 76 7 7 6 76 7 7 6 76 7 7 6 76 7 7 6 54 5 5 4 r nR 1 ð14:3-17Þ
Returning to Eq. (14.3-13) and using Eq. (14.3-14), we express ðq1 Þyðk þ dÞ as follows: ðq1 Þyðk þ dÞ ¼ Aðq1 ÞSðq1 Þyðk þ dÞ þ qd Rðq1 Þyðk þ dÞ ¼ Bðq1 ÞSðq1 ÞuðkÞ þ Rðq1 ÞyðkÞ
ð14:3-18Þ
Let ðq1 Þ ¼ Bðq1 ÞSðq1 Þ¼ b0 þ q1
ðq1 Þ¼ b0 þ
1q
1
þ þ
dþnB 1 q
ðdþnB 1Þ
ð14:3-19Þ where 1
¼ b0 s 1 þ b1 ;
2
¼ b0 s 2 þ b1 s 1 þ b2 ; . . . ;
dþnB 1
¼ bnB sd1 ð14:3-20Þ
Finally, we have ðq1 Þyðk þ dÞ ¼ b0 uðkÞ þ
ðq1 Þuðk 1Þ þ Rðq1 ÞyðkÞ
ð14:3-21Þ
Note that the right-hand side of Eq. (14.3-21) is the function g appearing in Eq. (14.3-13). Equation (14.3-21) can also be written as ðq1 Þyðk þ dÞ ¼ hT uðkÞ ¼ b0 uðkÞ þ hT0 u0 ðkÞ where
. h ¼ b0 .. T
1; . . . ;
dþnB 1 ; r0 ; r1 ; . . . ; rnR
ð14:3-22Þ
. ¼ b0 .. bthetaT0
ð14:3-23Þ
and uðkÞ is the so-called regression vector having the form . uT ðkÞ ¼ uðkÞ .. uðk 1Þ; . . . ; uðk d nB þ 1Þ; yðkÞ; yðk 1Þ; . . . ; yðk nR Þ . ð14:3-24Þ ¼ uðkÞ .. uT0 ðkÞ
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In what follows, we seek to find a control law uðkÞ, which drives the filtered plant-model error eðk þ dÞ ¼ ðq1 Þeðk þ dÞ to zero. Using Eqs (14.3-21) and (14.3-22), we have ðq1 Þeðk þ dÞ ¼ ðq1 yðk þ dÞ ðq1 Þym ðk þ dÞ ¼ b0 uðkÞ þ
ðq1 Þuðk 1Þ þ Rðq1 ÞyðkÞ ðq1 Þym ðk þ dÞ
¼ b0 uðkÞ þ hT0 u0 ðkÞ ðq1 Þym ðk þ dÞ Solving for uðkÞ, which drives the filtered error to eðk þ dÞ ¼ ðq1 Þeðk þ dÞ ¼ 0, we arrive at the desired control law: uðkÞ ¼
ðq1 Þym ðk þ dÞ Rðq1 ÞyðkÞ b0
ð14:3-25Þ zero,
ðq1 Þuðk 1Þ
i.e.,
ð14:3-26Þ
or equivalently uðkÞ ¼
ðq1 Þym ðk þ dÞ hT0 u0 ðkÞ b0
ð14:3-27Þ
where use has been made of the fact that b0 6¼ 0. Finally, using the fact that ðq1 Þ ¼ Bðq1 ÞSðq1 Þ, the expression for uðkÞ becomes uðkÞ ¼
1
1 ðq Þym ðk þ dÞ Rðq1 ÞyðkÞ 1 Bðq ÞSðq Þ 1
ð14:3-28Þ
From this last expression for uðkÞ, it is readily seen why the process should be minimum phase, as the system zeros appear in the denominator of the control law. It can be seen that the control law (14.3-26), which satisfies the control objective ðq1 Þeðk þ dÞ ¼ 0, also minimizes the quadratic performance index
2 Jðk þ dÞ ¼ ðq1 Þ½yðk þ dÞ ym ðk þ dÞ ð14:3-29Þ thereby assuring that Jðk þ dÞ 0, for k 0. The control scheme analyzed above for the case of known parameters is shown in Figure 14.3. 14.3.4 Hyperstability Design in the Case of Unknown Parameters 1 The Adaptation Algorithm When the system parameters appearing in the polynomials Aðq1 Þ and Bðq1 Þ are unknown, we keep the same structure for the controller, but replace the constant b0 and the vector h0 (which are now unknown) in Eq. (14.3-27), with the time-varying adjustable parameters h i b^0 ðkÞ and h^ T0 ðkÞ ¼ ^ 1 ðkÞ; . . . ; ^ dþnB 1 ðkÞ; r^0 ðkÞ; r^1 ðkÞ; . . . ; r^nR ðkÞ ð14:3-30Þ This procedure is widely known in the literature as the certainty equivalence principle. The adjustable parameters of Eq. (14.3-30) will be appropriately updated by the adaptation mechanism. The certainty equivalence control law now becomes
614
Chapter 14
Figure 14.3
The control scheme for tracking and regulation with independent dynamics for the case of known parameters.
uðkÞ ¼
ðq1 Þym ðk þ dÞ h^ T0 ðkÞu0 ðkÞ b^0 ðkÞ
ð14:3-31Þ
For convenience, we keep the same notation uðkÞ for the certainty equivalence control law (14.3-31) as well. Expression (14.3-31) may also be written as ðq1 Þym ðk þ dÞ ¼ h^ T ðkÞuðkÞ
ð14:3-32Þ
where
. ^hT ðkÞ ¼ b^0 ðkÞ .. h^ T0 ðkÞ
ð14:3-33Þ
In the case of unknown plant parameters, it is not possible to keep the filtered error eðk þ dÞ ¼ ðq1 Þeðk þ dÞ identically equal to zero. The design objective now changes and becomes that of finding a suitable adaptation mechanism for the adjustable parameters in Eq. (14.3-33), which will secure the asymptotic convergence of eðk þ dÞ to zero, with bounded input and output signals. Consequently, in the case of unknown plant parameters the control objective becomes lim eðk þ dÞ ¼ lim ðq1 Þ½yðk þ dÞ ym ðk þ dÞ ¼ 0;
k!þ1
k!þ1
8eð0Þ 6¼ 0;
h^ ð0Þ 2 RdþnB þnR þ1 ð14:3-34Þ
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615
with kuðkÞk bounded by all k. Then, if Eq. (14.3-34) holds, since ðq1 Þ is an asymptotically stable polynomial, one could conclude that limk!þ1 eðkÞ ¼ 0. That is, the plant-model error vanishes asymptotically. Using Eqs (14.3-22) and (14.3-32), the filtered plant-model error (or a priori adaptation error) eðk þ dÞ is expressed as eðk þ dÞ ¼ ðq1 Þeðk þ dÞ ¼ ðq1 Þ½yðk þ dÞ ym ðk þ dÞ ¼ hT uðkÞ h^ T ðkÞuðkÞ or T eðk þ dÞ ¼ h h^ ðkÞ uðkÞ
ð14:3-35Þ
Equivalently T eðkÞ ¼ ðq1 ÞeðkÞ ¼ h h^ ðk dÞ uðk dÞ
ð14:3-36Þ
Define the auxiliary error eðkÞ as T "ðkÞ ¼ h^ ðk dÞ h^ ðkÞ uðk dÞ
ð14:3-37Þ
and the a posteriori filtered plant-model error or augmented error "ðkÞ as T "ðkÞ ¼ eðkÞ þ "ðkÞ ¼ h h^ ðkÞ uðk dÞ
ð14:3-38Þ
By using the so-called hyperstability approach not presented herein, but analyzed in [12], it can be proven that the following adaptation algorithm: h^ ðkÞ ¼ h^ ðk 1Þ þ FðkÞuðk dÞ"ðkÞ
ð14:3-39Þ
assures that, for all eð0Þ 6¼ 0 and h^ ð0Þ 2 RdþnB þnR þ1 , we have lim "ðkÞ ¼ 0
k!þ1
and
lim eðkÞ ¼ lim eðkÞ ¼ 0
k!þ1
ð14:3-40Þ
k!þ1
The gain matrix FðkÞ is positive definite and is generated by F1 ðk þ 1Þ ¼ 1 ðkÞF1 ðkÞ þ 2 ðkÞuðk dÞuT ðk dÞ
when
Fð0Þ > 0 ð14:3-41Þ
with 0 < 1 ðkÞ 1
and
0 2 ðkÞ < 2
for all k
ð14:3-42Þ
Clearly, relation (14.3-40) states that the control objective is satisfied asymptotically. Note that the algorithm presented above is a special case of the algorithm given by Ionescu & Monopoli in [19], who introduced the notion of the augmented error for the first time for discrete-time systems. Remark 14.3.2 To apply the adaptation algorithm (14.3-39), an implementable form for the a posteriori filtered plant-model error "ðkÞ may be derived using Eqs (14.3-32) and (14.339), as follows:
616
Chapter 14
T "ðkÞ ¼ eðkÞ þ "ðkÞ ¼ ðq1 ÞðyðkÞ ym ðkÞÞ þ h^ ðk dÞ h^ ðkÞ uðk dÞ T ¼ ðq1 ÞyðkÞ h^ T ðk dÞuðk dÞ þ h^ ðk dÞ h^ ðkÞ uðk dÞ ¼ ðq1 ÞyðkÞ h^ T ðkÞuðk dÞ ¼ ðq1 ÞyðkÞ h^ T ðk 1Þuðk dÞ uT ðk dÞFðkÞuðk dÞ"ðkÞ
ð14:3-43Þ
Hence "ðkÞ ¼
"~ðkÞ 1 þ u ðk dÞFðkÞuðk dÞ T
ð14:3-44Þ
where "~ðkÞ ¼ ðq1 ÞyðkÞ h^ T ðk 1Þuðk dÞ
ð14:3-45Þ
Remark 14.3.3 During the adaptation procedure we have b^0 ðkÞ ¼ 0. To avoid division by zero in Eq. (14.3-31), if jb^0 ðkÞj < ð > 0Þ for a certain k, we repeat evaluating h^ ðkÞ from Eq. (14.3-39), using appropriate values for 1 ðk 1Þ and 2 ðk 1Þ in Eq. (14.3-41). These values must be chosen by trial and error so that jb0 ðkÞj . The control algorithm is summarized in Table 14.1. The control scheme for tracking and regulation with independently chosen dynamics, for the case of unknown parameters, is given in Figure 14.4.
Table 14.1
The Model Reference Adaptive Control (MRAC) Algorithm
Algorithm
Equation No.
. uT ðkÞ ¼ ½uðkÞ .. uðk 1Þ; . . . ; uðk d nB þ 1Þ; yðkÞ; yðk 1Þ; . . . ; yðk nR Þ . ¼ ½uðkÞ .. uT0 ðkÞ
ð14:3-46Þ
. h^ T ðkÞ ¼ ½b^0 ðkÞ ..
h^ T0 ðkÞ
(14.3-47)
ðq1 Þym ðk þ dÞ h^ T0 ðkÞu0 ðkÞ b^0 ðkÞ
(14.3-48)
uðkÞ ¼
"~ðkÞ ¼ ðq1 ÞyðkÞ h^ T ðk 1Þu0 ðk dÞ "ðkÞ ¼
1þ
uT ðk
"~ðkÞ dÞFðkÞuðk dÞ
h^ ðkÞ ¼ h^ ðk 1Þ þ FðkÞuðk dÞ"ðkÞ F1 ðk þ 1Þ ¼ 1 F1 ðkÞ þ 2 ðkÞuðk dÞuT ðk dÞ with initial conditions yð0Þ; Fð0Þ > 0; h^ ð0Þ
(14.3-49) (14.3-50) (14.3-51) (14.3-52)
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Figure 14.4 The control scheme for tracking and regulation with independent dynamics for the case of unknown parameters.
Example 14.3.1 Consider the system Aðq1 ÞyðkÞ ¼ q1 Bðq1 ÞuðkÞ;
yð0Þ ¼ 1
where Aðq1 Þ ¼ 1 þ 2q1 þ q2 and Bðq1 Þ ¼ 2 þ q1 þ 0:5q2 (asymptotically stable). It is desired to track the output of the reference model " # " # q1 Bm ðq1 Þ 1 þ 0:3q1 1 ym ðkÞ ¼ rðkÞ ¼ q rðkÞ with ym ð0Þ ¼ 2 Am ðq1 Þ 1 q1 þ 0:25q2 The dynamics during regulation are characterized by the asymptotically stable polynomial ðq1 Þ ¼ 1 þ 0:5q1 . (a)
Determine a model reference control law which achieves the control objectives in the case of known parameters for Aðq1 Þand Bðq1 Þ.
618
Chapter 14
(b)
In the case of unknown plant parameters, determine a control law and appropriate adaptations (MRAC design) to satisfy the control objectives asymptotically.
Solution (a) Here d ¼ 1, Sðq1 Þ ¼ 1, and we are looking for Rðq1 Þ ¼ r0 þ r1 q1 such that ðq1 Þ ¼ 1 þ 0:5q1 ¼ ð1 þ 2q1 þ q2 Þ þ q1 ðr0 þ r1 q1 Þ ¼ Aðq1 ÞSðq1 Þ þ qd Rðq1 Þ or equivalently 2 32 3 2 3 1 1 0 0 1 4 a1 1 0 54 r0 5 ¼ 4 1 5 r1 0 a2 0 1
2 or
1 42 1
0 1 0
3 32 3 2 1 0 1 0 54 r0 5 ¼ 4 0:5 5 0 1 r1
One easily obtains r0 ¼ 1:5 and r1 ¼ 1. Moreover, ðq1 Þ ¼ Bðq1 ÞSðq1 Þ ¼ 2 þ q1 þ 0:5q2 ¼ 2 þ q1 ð1 þ 0:5q1 Þ ¼ b0 þ q1
ðq1 Þ
In the case of unknown parameters, the control law is ðq1 Þym ðk þ 1Þ Rðq1 ÞyðkÞ ðq1 Þuðk 1Þ b0 y ðk þ 1Þ þ 0:5ym ðkÞ þ 1:5yðkÞ þ yðk 1Þ uðk 1Þ 0:5uðk 2Þ ¼ m 2
uðkÞ ¼
(b) In the case of unknown plant parameters, the certainty equivalence control law is ym ðk þ 1Þ þ 0:5ym ðkÞ ^1 ðkÞuðk 1Þ ^ ðkÞuðk 2Þ ^3 ðkÞyðkÞ ^4 ðkÞyðk 1Þ uðkÞ ¼ 2 ^0 ðkÞ where
h^ T ðkÞ ¼ ^0 ðkÞ; ^1 ðkÞ; ^2 ðkÞ; ^3 ðkÞ; ^4 ðkÞ where h^ ðkÞ is appropriately changed at each step, by using the adaptation algorithm given below. In this algorithm we let 1 ðkÞ ¼ 0:98 and 2 ðkÞ ¼ 1. This corresponds to a forgetting factor algorithm, as explained in the discussion of the parameter adaptation algorithm presented at the end of this section. The adaptation algorithm is Fð0Þ ¼
1 I5 103
uT ðkÞ ¼ ½uðkÞ; uðk 1Þ; uðk 2Þ; yðkÞ; yðk 1Þ
for
k ¼ 0; 1; 2; . . .
"~ðkÞ ¼ yðkÞ þ 0:5yðk 1Þ h^ T ðk 1Þuðk 1Þ
for
k ¼ 1; 2; . . .
"ðkÞ ¼
1þ
uT ðk
"~ðkÞ 1ÞFðkÞuðk 1Þ
for
k ¼ 1; 2; . . .
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h^ ðkÞ ¼ h^ ðk 1Þ þ FðkÞuðk 1Þ"ðkÞ
k ¼ 1; 2; . . .
for
F1 ðk þ 1Þ ¼ 0:98F1 ðkÞ þ uðk 1ÞuT ðk 1Þ
for
k ¼ 0; 1; 2; . . .
We can initialize h^ ðkÞ with h^ T ð0Þ ¼ ½1; 0; 0; 1; 1 for convenience The particular choice made for the adaptation algorithm was guided by the objective of global asymptotic stability for the whole system (14.3-39), (14.3-41), (14.3-42), (14.3-44), i.e., asymptotic stability for any finite initial parameter error and plant-model error. Moreover, the adaptation mechanism should ensure that the error between the plant output and the ouput of the reference model tends to zero asymptotically, which is the control objective. The approach applied to satisfy the aforementioned objectives relies upon the hyperstability theory presented in [12]. Global asymptotic stability is guaranteed. Indeed, most adaptive control schemes, after an adequate analysis, lead to an equation of the form T vðkÞ ¼ Hðq1 Þ h h^ ðkÞ uðk dÞ
ð14:3-53Þ
where h is an unknown parameter vector, h^ ðkÞ is the estimate of h resulting from an appropriate parameter adaptation algorithm, uðk dÞ is a measurable regressor vector, Hðq1 Þ is a rational discrete transfer function of the form Hðz1 Þ ¼
1 þ h10 z1 þ þh0 z 1 þ h1 z1 þ þ h z
ð14:3-54Þ
and the measurable quantity vðkÞ is the so-called processed augmented error. A particular case of Eq. (14.3-53) is given by Eq. (14.3-38), where Hðq1 Þ ¼ 1 and the a posteriori filtered plant-model error "ðkÞ takes the place of vðkÞ. Then, a stability theorem given in [22] provides the following appropriate adaptation mechanism sought, which makes use of vðkÞ as the basis of the parameter update law for h^ ðkÞ: h^ ðkÞ ¼ h^ ðk 1Þ þ FðkÞuðk dÞvðkÞ F1 ðk þ 1Þ ¼ 1 ðkÞF1 ðkÞ þ 2 ðkÞuðk dÞuT ðk dÞ;
ð14:3-55Þ Fð0Þ > 0
ð14:3-56Þ
with 0 < 1 ðkÞ 1;
0 2 ðkÞ < 2;
8k
ð14:3-57Þ
The convergence of the plant-model error eðkÞ to zero and the boundedness of the input and output signals can be proved. 2 Discussion of the Parameter Adaptation Algorithm Expression (14.3-56) defines a general law for the determination of the adaptation gain matrix FðkÞ and is repeated here for convenience: F1 ðk þ 1Þ ¼ 1 ðkÞF1 ðkÞ þ 2 ðkÞuðk dÞuT ðk dÞ
with
Fð0Þ > 0 ð14:3-58Þ
620
Chapter 14
where 0 < 1 ðkÞ 1 and 0 2 ðkÞ < 2. Using the matrix inversion lemma of Chapter 13 (relation 13.3-7)), the above equation may be written equivalently as follows: " # 1 FðkÞuðk dÞuT ðk dÞFðkÞ FðkÞ Fðk þ 1Þ ¼ ð14:3-59Þ T 1 ðkÞ 1 ðkÞ1 2 ðkÞ þ u ðk dÞFðkÞuðk dÞ We note that, in general, 1 ðkÞ and 2 ðkÞ have opposite effects on the adaptation gain. That is, as 1 ðkÞ 1 increases, the gain 2 ðkÞ does the opposite, i.e., it decreases the gain. Different types of adaptation algorithms are obtained by appropriate choices of 1 ðkÞ and 2 ðkÞ, 0 < 1 ðkÞ 1, 0 2 ðkÞ < 2. We distinguish the following choices: 1.
2. 3.
4.
5.
1 ðkÞ 1 and 2 ðkÞ 0. In this case FðkÞ ¼ Fð0Þ. This choice corresponds to an algorithm with a constant gain. It is the simplest to implement, but also the least efficient. It is convenient for the estimation of unknown constant parameters, but not for time-varying parameters. 1 ðkÞ ¼ 2 ðkÞ 1. This choice corresponds to a recursive least-squares algorithm with decreasing gain. 1 ðkÞ 1 < 1 (usually 0:95 1 0:99) and 2 ðkÞ 1. This choice corresponds to an algorithm with a constant forgetting factor 1 (it ‘‘forgets’’ old measurements exponentially). 1 ðkÞ < 1 and 2 ðkÞ 1. This choice corresponds to a variable forgetting or factor type of algorithm. Usually, 0:95 1 ðkÞ 0:99 with 0:95 0 0:99 and 1 ðk þ 1Þ ¼ 0 1 ðkÞ þ 1 0 , 0:95 1 ð0Þ 0:99A. In this last case it holds true that limk!þ1 1 ðkÞ ¼ 1. When both 1 ðkÞ and 2 ðkÞ are time varying, we have extra freedom in choosing the gain profiles. For example, by choosing 1 ðkÞ=2 ðkÞ ¼ ðkÞ, we have the following expression for the trace of FðkÞ: " # 1 FðkÞuðk dÞuT ðk dÞFðkÞ tr Fðk þ 1Þ ¼ tr FðkÞ 1 ðkÞ ðkÞ þ uT ðk dÞFðkÞuðk dÞ ð14:3-60Þ
At each step we can choose ðkÞ ¼ 1 ðkÞ=2 ðkÞ and then specify 1 ðkÞ from Eq. (14.3-60) such that the trace of FðkÞ has a prespecified value (constant or time varying) at each step. Remark 14.3.4 We note that when uðk dÞ ¼ 0 for a long period of time (this may happen in the steady state or in the absence of any signal in the input), using choices 3 or 4 may lead to an undesirable increase in the adaptation gain. In this case there is no change in the parameter estimates and FðkÞ will grow exponentially if 1 ðkÞ < 1, since in this case we have that Fðk þ 1Þ ¼ 1=1 1 ðkÞFðkÞ. A new change in the set point can then lead to large changes in the parameter estimates and the plant output.
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621
Remark 14.3.5 In practice we initialize at Fð0Þ ¼ ð1=ÞI, 0 < 1.
14.4
SELF-TUNING REGULATORS
14.4.1 Introduction Another important class of adaptive systems with many industrial applications is that of STRs. The block diagram of an STR is shown in Figure 14.5. The STR is based on the idea of separating the estimation of the unknown parameters of the system under control, from the design of the controller. The control scheme consists of two loops: the ‘‘inner’’ loop, which involves the plant with unknown parameters and a linear feedback controller with adjustable parameters and the ‘‘outer‘‘ loop, which is used in the case of unknown plant parameters and is composed of a recursive parameter estimator and a block named ‘‘controller design.’’ In the case of known plant parameters, the design of the controller (i.e., the determination of its parameters as functions of the plant parameters) is carried out off-line. This controller satisfies a specific control design problem, such as minimum variance, pole placement, model following, etc. This control problem, in the context of the STRs, is called the underlying control problem. When the plant parameters are uncertain, the recursive parameter estimator provides on-line estimates of the unknown plant parameters. On the basis of these estimates, the solution of the control design problem (i.e., the determination of the controller parameters as functions of the plant parameters) is achieved on-line in each step by the ‘‘controller design’’ block. The controller parameter estimates thus obtained, are used to recalculate the control law at each step. Apart from the fact
Figure 14.5
Block diagram of a self-tuning regulator (STR).
622
Chapter 14
that the controller parameters are substituted by estimates obtained by the on-line solution of the control design problem, the controller structure is kept the same as in the case of known plant parameters. This is the certainty equivalence principle. For the estimation of the plant parameters, various schemes can be used: least squares, recursive least squares, maximum likelihood, extended Kalman filtering, etc. Different combinations of appropriate parameter estimation methods and suitable control strategies lead to different adaptive controllers. For example, an adaptive controller based on least-squares estimation and deadbeat control was first described by Kalman in 1958, while the original STR design by A stro¨m et al. [16] was based on least-squares estimation and the minimum-variance control problem. The control procedure discussed above leads to an explicit STR, where the term explicit is used because the plant parameters are estimated explicitly. Such explicit STRs need to solve, at each step, the tedious controller design problem. It is sometimes possible, in order to eliminate the design calculations, to reparametrize the plant model, so that it can be expressed in terms of the controller parameters, which are then updated directly by the estimator. This leads to implicit STRs. Implicit STRs avoid controller design calculations and are based on estimates of an implicit plant model. Explicit STRs correspond to indirect adaptive control, while implicit STRs correspond to direct adaptive control. A close relation has been established between STRs and MRAC systems, in spite of differences in their origin. Indeed, MRAC design was based on the deterministic servoproblem, while STR design was based on the stochastic regulation problem. Although the design methods of the ‘‘inner’’ loop and the parameter adjustments in the ‘‘outer’’ loop are different, direct MRAC systems are closely related to implicit STRs, while indirect MRAC systems are related to explicit STRs. Implicit STRs are not discussed here. Explicit STRs, using the pole placement technique, are treated in Subsec. 14.4.2 that follows. Furthermore, it is explained how implicit pole-placement designs can also be derived. 14.4.2
Pole-Placement Self-Tuning Regulators
1 Pole-Placement Design with Known Parameters The pole-placement design (chosen as the underlying control problem), can be applied for nonminimum phase systems. The procedure consists of finding a feedback law for which the closed-loop poles have desired locations. Both explicit and implicit schemes may be formulated. Explicit schemes are based on estimates of parameters in an explicit system model, while implicit schemes are based on estimates of parameters in a modified implicit system model. Similarities between MRAC and STRs will emerge. The discussion is limited to SISO systems described by Aðq1 ÞyðkÞ ¼ qd Bðq1 ÞuðkÞ
ð14:4-1Þ
where Aðq1 Þ ¼ 1 þ a1 q1 þ þ anA qnA
ð14:4-2Þ
Bðq1 Þ ¼ b0 þ b1 q1 þ þ bnB qnB
ð14:4-3Þ
Adaptive Control
623
The polynomial Aðq1 Þ is thus monic, Aðq1 Þ and Bðq1 Þ are relatively prime (have no common factors), and the system’s delay is d 1. It is desired to find a controller for which the relation from the command signal rðkÞ to the output yðkÞ becomes Am ðq1 ÞyðkÞ ¼ qd Bm ðq1 ÞrðkÞ 1
ð14:4-4Þ 1
1
where Am ðq Þ is a stable monic polynomial and Am ðq Þ and Bm ðq Þ are relatively prime. Restrictions on Bm ðq1 Þ will appear in what follows. A general structure ðRST canonical structure) for the controller is presented in Figure 14.6. The controller is described by Rðq1 ÞuðkÞ ¼ Tðq1 ÞrðkÞ Sðq1 ÞyðkÞ
ð14:4-5Þ
This controller offers a negative feedback with transfer function Sðq1 Þ=Rðq1 Þ and feedforward with transfer function Tðq1 Þ=Rðq1 Þ. Multiplying Eq. (14.4-5) by qd Bðq1 Þ, one obtains qd Bðq1 ÞRðq1 ÞuðkÞ ¼ qd Tðq1 ÞBðq1 ÞrðkÞ qd Sðq1 ÞBðq1 ÞyðkÞ or
h i A q1 Rðq1 Þ þ qd Bðq1 ÞSðq1 Þ yðkÞ ¼ qd Tðq1 ÞBðq1 ÞrðkÞ
ð14:4-6Þ
Hence, the relation between yðkÞ and rðkÞ is given by yðkÞ qd Tðq1 ÞBðq1 Þ ¼ rðkÞ Aðq1 ÞRðq1 Þ þ qd Bðq1 ÞSðq1 Þ
ð14:4-7Þ
Relation (14.4-4), which represents the desired behavior, may be written as yðkÞ qd Bm ðq1 Þ ¼ rðkÞ Am ðq1 Þ
ð14:4-8Þ
Thus, the design problem is equivalent to the algebraic problem of finding Rðq1 Þ, Sðq1 Þ, and Tðq1 Þ, for which the following equation holds true: qd Tðq1 ÞBðq1 Þ qd Bm ðq1 Þ ¼ d 1 1 Am ðq1 Þ þ q Bðq ÞSðq Þ
Aðq1 ÞRðq1 Þ
ð14:4-9Þ
From the left-hand side of Eq. (14.4-9), it is evident that the system zeros ðznB Bðz1 Þ ¼ 0) will also be closed-loop zeros, unless they are canceled out by corre-
Figure 14.6
The RST canonical structure used for the controller.
624
Chapter 14
sponding closed-loop poles. But unstable (or poorly damped) zeros should not be canceled out by the controller, since they would lead to instability. Thus, let us factor out Bðq1 Þ as follows Bðq1 Þ ¼ Bþ ðq1 ÞB ðq1 Þ þ
ð14:4-10Þ
1
where B ðq Þ contains the well-damped zeros (which are canceled out) and B ðq1 Þ contains the unstable and poorly damped zeros (which are not canceled out). To obtain a unique factorization, we also require that Bþ ðq1 Þ is a monic polynomial. From Eq. (14.4-9), it follows that the characteristic polynomial of the closed-loop system is Aðq1 ÞRðq1 Þ þ qd Bðq1 ÞSðq1 Þ
ð14:4-11Þ
The factors of this polynomial should be the desired reference model poles, i.e., the roots of Am ðq1 Þ, and the system zeros which can be canceled out, i.e., the roots of Bþ ðq1 Þ. Moreover, since in general, the order of the reference model ðdeg Am ðq1 ÞÞ, is less than the order of the closed-loop system degðAðq1 ÞRðq1 Þ þ qd Bðq1 ÞSðq1 ÞÞ, there are factors in the left-hand side of Eq. (14.4-9) which cancel out. These factors correspond to a polynomial A0 ðq1 Þ. The polynomial A0 ðq1 Þ is called the observer polynomial and is chosen to have well-damped roots. The appearance of this polynomial is more evident when a state-space solution to this problem is considered. In this case, the solution is a combination of state feedback and an observer. Hence, the characteristic polynomial of the closedloop system assumes the form Aðq1 ÞRðq1 Þ þ qd Bðq1 ÞSðq1 Þ ¼ Bþ ðq1 ÞAm ðq1 ÞA0 ðq1 Þ
ð14:4-12Þ
Now, since Bþ ðq1 Þ is the divident of Bðq1 Þ and the polynomials Aðq1 Þ and Bðq Þ are relatively prime, it is clear from Eq. (14.4-12) that Bþ ðq1 Þ should also be the dividend of the polynomial Rðq1 Þ, i.e., 1
Rðq1 Þ ¼ Bþ ðq1 ÞR1 ðq1 Þ
ð14:4-13Þ
Equation (14.4-12) may then be rewritten as Aðq1 ÞR1 ðq1 Þ þ qd B ðq1 ÞSðq1 Þ ¼ Am ðq1 ÞA0 ðq1 Þ
ð14:4-14Þ
Hence, Eq. (14.4-9) is then equivalent to the equation qd Bþ ðq1 ÞB ðq1 ÞTðq1 Þ qd Bm ðq1 Þ ¼ Bþ ðq1 ÞAm ðq1 ÞA0 ðq1 Þ Am ðq1 Þ
ð14:4-15Þ
In order that the foregoing equation holds true and since B ðq1 Þ cannot be canceled out, it is clear that B ðq1 Þ must be a factor of Bm ðq1 Þ i.e., 1 Bm ðq1 Þ ¼ B ðq1 ÞBþ m ðq Þ
ð14:4-16Þ
and also that 1 Tðq1 Þ ¼ A0 ðq1 ÞBþ m ðq Þ
ð14:4-17Þ 1
It should be evident that we are not absolutely free in the choice of Bm ðq Þ, which corresponds to the specifications for the closed-loop zeros. We can choose freely the
Adaptive Control
625
1 1 part Bþ m ðq Þ of Bm ðq Þ while Eq. (14.4-16) should be valid, otherwise there is no solution to the design problem. It is necessary to establish conditions under which a solution for the polynomials R1 ðq1 Þ and Sðq1 Þ in Eq. (14.4-14), is guaranteed. This equation, linear in the polynomials R1 ðq1 Þ and Sðq1 Þ is a special case of the Diophantine equation (or Bezout identity), which has the general form (see also Remark 14.3.1):
A ðq1 ÞR ðq1 Þ þ B ðq1 ÞS ðq1 Þ ¼ C ðq1 Þ
ð14:4-18Þ
It can be proved that the Diophantine equation (14.4-18) always has a solution for R ðq1 Þ and S ðq1 Þ, if the greatest common factor of A ðq1 Þ and B ðq1 Þ is a dividend of C ðq1 Þ. Therefore, Eq. (14.4-14) will always have a solution for R1 ðq1 Þ and Sðq1 Þ, since we have assumed that Aðq1 Þ and Bðq1 Þ are coprime and, consequently, Aðq1 Þ and qd B ðq1 Þ are also coprime. Note that if a solution exists, then Eq. (14.4-18), in general, has infinitely many solutions. Indeed, if R0 ðq1 Þ and S 0 ðq1 Þ are solutions of Eq. (14.4-18), then it can be easily verified that R0 ðq1 Þ þ B ðq1 ÞQðq1 Þ and S 0 ðq1 Þ A ðq1 ÞQðq1 Þ, with Qðq1 Þ an arbitrary polynomial, are also solutions of Eq. (14.4-18). Particular solutions can be specified in several ways. Different solutions give systems with different noise rejection properties. It can be proved that there are unique solutions to Eq. (14.4-18) if, in addition, we impose the following restriction for the solution sought: deg R ðq1 Þ < deg B ðq1 Þ
ð14:4-19Þ
deg S ðq1 Þ < deg A ðq1 Þ
ð14:4-20Þ
or
Moreover, for the pole placement control problem, we seek particular solutions which lead to causal control laws (i.e., deg Sðq1 Þ deg Rðq1 Þ and deg Tðq1 Þ deg Rðq1 ÞÞ. Note also that it is often advantageous to keep deg Sðq1 Þ ¼ deg Tðq1 Þ ¼ deg Rðq1 Þ, in order to avoid an unnecessary delay in the controller. Note that from Eq. (14.4-12) we must select either deg Rðq1 Þ ¼ deg Am ðq1 Þ þ deg A0 ðq1 Þ þ deg Bþ ðq1 Þ deg Aðq1 Þ ð14:4-21Þ or deg Sðq1 Þ ¼ deg Am ðq1 Þ þ deg A0 ðq1 Þ deg B ðq1 Þ d 1
1
ð14:4-22Þ
The degrees of Rðq Þ and Sðq Þ are imposed by the structure of the system and the structure of the desired closed-loop transfer function. To assure unique solutions, using Eq. (14.4-21) we must have deg Sðq1 Þ deg Aðq1 Þ 1 (this results from Eq. (14.4-20)), and if we choose to satisfy Eq. (14.4-22) we must have deg Rðq1 Þ deg Bðq1 Þ þ d 1 (this results from Eq. (14.4-19)). By selecting Eq. (14.4-21) or Eq. (14.4-22), possible choices for the degrees of R1 ðq1 Þ and Sðq1 Þ in Eq. (14.4-14), corresponding to unique solutions and minimum-order polynomials, are consequently given below:
626
Chapter 14
deg R1 ðq1 Þ ¼ deg Am ðq1 Þ þ deg A0 ðq1 Þ deg Aðq1 Þ
ð14:4-23Þ
deg Sðq1 Þ ¼ deg Aðq1 Þ 1
ð14:4-24Þ
deg R1 ðq1 Þ ¼ deg B ðq1 Þ þ d 1
ð14:4-25Þ
deg Sðq1 Þ ¼ deg Am ðq1 Þ þ deg A0 ðq1 Þ deg B ðq1 Þ d
ð14:4-26Þ
or
By selecting Eqs (14.4-23) and (14.4-24), and in order to have causal control laws (that is deg Sðq1 Þ ¼ deg Aðq1 Þ 1 deg Rðq1 ÞÞ, relation (14.4-21) leads to deg A0 ðq1 Þ 2 deg Aðq1 Þ deg Am ðq1 Þ deg Bþ ðq1 Þ 1
ð14:4-27Þ
Relation (14.4-27) is a restriction on the degree of the observer polynomial A0 ðq1 Þ. Moreover, requiring that deg Tðq1 Þ deg Rðq1 Þ and using Eqs (14.4-17) and (14.4-21), we obtain 1 1 1 deg A0 ðq1 Þ þ deg Bþ m ðq Þ ¼ deg Tðq Þ deg Rðq Þ
¼ deg Am ðq1 Þ þ deg A0 ðq1 Þ þ deg Bþ ðq1 Þ deg Aðq1 Þ or deg Aðq1 Þ deg Bðq1 Þ deg Am ðq1 Þ deg Bm ðq1 Þ
ð14:4-28Þ
The pole excess of the system should be less than the pole excess of the reference model. Condition (14.4-27) in combination with Eq. (14.4-28) guarantees that the feedback will be causal when Eqs (14.4-23) and (14.4-24) are chosen. This, in turn, implies that the transfer functions S=R and T=R will be causal. The control algorithm, in the case of known parameters, is summarized in Table 14.2.
Table 14.2
The Pole-Placement Control Algorithm for the Case of Known Parameters*
Step 1
Factor Bðq1 Þ ¼ Bþ ðq1 ÞB ðq1 Þ with Bþ ðq1 Þ monic. Choose Am ðq1 Þ, 1 1 Bm ðq1 Þ ¼ B ðq1 ÞBþ m ðq Þ and A0 ðq Þ such that Eqs (14.4-27) and (14.4-28) are satisfied
Step 2
Select the degrees of R1 ðq1 Þ and Sðq1 Þ in order to satisfy Eqs (14.4-23) and (14.4-24) or Eqs (14.4-25) and (14.4-26). Solve Aðq1 ÞR1 ðq1 Þ þ qd B ðq1 ÞSðq1 Þ ¼ Am ðq1 ÞA0 ðq1 Þ for R1 ðq1 Þ and Sðq1 Þ
Step 3 Step 4
1 Compute Rðq1 Þ ¼ Bþ ðq1 ÞR1 ðq1 Þ and Tðq1 Þ ¼ A0 ðq1 ÞBþ m ðq Þ. The foregoing steps are executed once off-line.
Apply the control law " # " # Tðq1 Þ Sðq1 Þ uðkÞ ¼ rðkÞ yðkÞ Rðq1 Þ Rðq1 Þ
*Given Aðq1 Þ and Bðq1 Þ monic and Aðq1 Þ and Bðq1 Þ co prime.
at each step
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2 Pole-Placement Design in the Case of Unknown Parameters In the case of uncertain system model parameters, an STR is used on the basis of the following separation principle. Here, the unknown system parameters are estimated recursively. Based on the certainty equivalence principle, the controller is recomputed at each step using the estimated system parameters. The controller design problem (Diophantine equation) is therefore solved at each step. The parameter estimator is based on the system model Aðq1 ÞyðkÞ ¼ Bðq1 Þuðk dÞ
ð14:4-29Þ
or explicitly yðkÞ þ a1 yðk 1Þ þ þ anA yðk nA Þ ¼ b0 uðk dÞ þ b1 uðk d 1Þ þ þ bnB uðk d nB Þ ð14:4-30Þ Introducing the parameter vector
hT ¼ a1 ; . . . ; an A ; b0 ; . . . ; bn B
ð14:4-31Þ
and the regression vector uT ðkÞ ¼ ½yðk 1Þ; . . . ; yðk nA Þ; uðk dÞ; . . . ; uðk d nB Þ
ð14:4-32Þ
Eq. (14.4-30) is expressed compactly as yðkÞ ¼ hT uðkÞ
ð14:4-33Þ
Based on the prediction model (14.4-33), the recursive least-squares estimator is described by the recursive equation h^ ðkÞ ¼ h^ ðk 1Þ þ FðkÞuðkÞ"ðkÞ
ð14:4-34Þ
with prediction error "ðkÞ ¼ yðkÞ uT ðkÞh^ ðk 1Þ
ð14:4-35Þ
The gain matrix FðkÞ can be deduced recursively using the expression 1 FðkÞuðkÞuT ðkÞFðkÞ FðkÞ ; Fð0Þ > 0 ð14:4-36Þ Fðk þ 1Þ ¼ 1 þ uT ðkÞFðkÞuðkÞ where 0 < 1 is a forgetting factor. The restrictions of Remark 14.3.4 hold for Eq. (14.4-36). In self-tuning, the convergence of the parameter estimates to the true values is of great importance. To obtain good estimates using Eq. (14.4-34), it is necessary that the process input be sufficiently rich in frequencies, or persistently exciting. The concept of persistent excitation was first introduced in identification problems. This states that we cannot identify all the parameters of a model unless we have enough distinct frequencies in the spectrum of the input signal. In general, when the input to a system is the result of feedback and is therefore a dependent variable within the adaptive loop, the input signal is not persistently exciting. In the explicit STR based on the pole-placement design discussed above, the estimated parameters are the parameters of the system model. This explicit adaptive pole-placement algorithm is summarized in Table 14.3.
628
Chapter 14
Table 14.3
The Explicit Adaptive Pole-Placement Algorithm for the Case of Unknown
Parameters Step 1
Estimate the model parameters in Aðq1 Þ and Bðq1 Þ using Eqs (14.4-34), (14.4-35), and (14.4-36), recursively, at each step. It is assumed that Aðq1 Þ and Bðq1 Þ have no common factors
Step 2
Factorize the polynomial Bðq1 Þ so that the decomposition Bþ ðq1 ÞB ðq1 Þ can be made ON-LINE at each step. Solve the controller design problem with the estimates obtained in step 1, i.e., solve Eq. (14.4-14) for R1 ðq1 Þ and Sðq1 Þ using Aðq1 Þ and B ðq1 Þ calculated on the basis of the estimation at step. 1. Calculate Rðq1 Þ and Tðq1 Þ from Eqs (14.4-13) and (14.4-17), respectively
Step 3
Compute the control law using Eq. (14.4-5)
Step 4
Repeat steps 1–3 at each sampling period
An implicit STR design procedure based on pole placement may also be considered. To this end, we reparametrize the system model such that the controller parameters appear. These controller parameters can then be updated directly. The proper system model structure sought is obtained by multiplying Eq. (14.4-14) by yðkÞ to yield Am ðq1 ÞA0 ðq1 ÞyðkÞ ¼ Aðq1 ÞR1 ðq1 ÞyðkÞ þ qd B ðq1 ÞSðq1 ÞyðkÞ ¼ qd Bðq1 ÞR1 ðq1 ÞuðkÞ þ qd B ðq1 ÞSðq1 ÞyðkÞ
ð14:4-37Þ ¼ qd B ðq1 Þ Rðq1 ÞuðkÞ þ Sðq1 ÞyðkÞ The reparametrization (14.4-37), which is an implicit system model, is redundant, since it has more parameters than Eq. (14.4-29). It is also bilinear in the parameters of B ðq1 Þ, Rðq1 Þ, and Sðq1 Þ. This leads to a nontrivial bilinear estimation problem. We can obtain the regulator parameters by estimating B ðq1 Þ, Rðq1 Þ, and Sðq1 Þ in Eq. (14.4-37) directly, avoiding at each step the control design problem, i.e., the solution of the Diophantine equation. This leads to a less timeconsuming algorithm, in the sense that the design calculations become trivial. The implicit STR is summarized in Table 14.4. To avoid nonlinear parametrization, Eq. (14.4-37) is rewritten equivalently as Am ðq1 ÞA0 ðq1 ÞyðkÞ ¼ qd R ðq1 ÞuðkÞ þ qd S ðq1 ÞyðkÞ
ð14:4-38Þ
where R ðq1 Þ ¼ B ðq1 ÞRðq1 Þ
ð14:4-39Þ
S ðq1 Þ ¼ B ðq1 ÞSðq1 Þ
ð14:4-40Þ
and
Based on the linear model (14.4-41), it is possible to estimate the coefficients of the polynomials R ðq1 Þ and S ðq1 Þ. However, it should be noted that, in general, this is not a minimal parametrization since the coefficients of the polynomial B ðq1 Þ are estimated twice. Moreover, possible common factors in R ðq1 Þ and S ðq1 Þ (corre-
Adaptive Control
Table 14.4
629
The Implicit Pole-Placement STR for the Case of Unknown Parameters
Step 1 Estimate the coefficients in Rðq1 Þ, B ðq1 Þ, and Sðq1 Þ recursively based on the reparametrized model (bilinear estimation problem)
Am ðq1 ÞA0 ðq1 ÞyðkÞ ¼ qd B ðq1 Þ½Rðq1 ÞuðkÞ þ Sðq1 ÞyðkÞ
ð14:4 41Þ
Step 2 Compute the control law using the relations 1 Tðq1 Þ ¼ A0 ðq1 ÞBþ m ðq Þ
ð14:4 42Þ
" # " # Tðq1 Þ Sðq1 Þ uðkÞ ¼ rðkÞ yðkÞ Rðq1 Þ Rðq1 Þ
ð14:4 43Þ
Step 3 Repeat steps 1 and 2 at each sampling period
sponding to B ðq1 Þ) should be canceled out to avoid cancellation of unstable modes in the control law. The algorithm thus obtained is summarized in Table 14.5. Example 14.4.1 Consider the system Aðq1 ÞyðkÞ ¼ q1 Bðq1 ÞuðkÞ 1
1
where Aðq Þ ¼ 1 þ 2q þ q can be factored as follows:
2
with
yð0Þ ¼ 1
1
and Bðq Þ ¼ 2 þ q1 þ q2 . The polynomial Bðq1 Þ
Bðq1 Þ ¼ 2ð1 þ 0:5q1 þ 0:5q2 Þ ¼ B ðq1 ÞBþ ðq1 Þ with Bþ ðq1 Þ monic. The desired closed-loop behavior is given by Am ðq1 ÞyðkÞ ¼ q1 Bm ðq1 ÞrðkÞ with Am ðq1 Þ ¼ 1 q1 þ 0:25q2
Table 14.5
An Alternate Implicit Pole-Placement STR for the Case of Unknown
Parameters Step 1
Using the model (14.4-41) and least-squares, estimate the coefficients of the polynomials R ðq1 Þ and S ðq1 Þ
Step 2
Cancel out possible common factors in R ðq1 Þ and S ðq1 Þ in order to obtain Rðq1 Þ and Sðq1 Þ
Step 3
Compute the control law using the relations 1 Tðq1 Þ ¼ A0 ðq1 ÞB m ðq Þ
" # " # Tðq1 Þ Sðq1 Þ rðkÞ yðkÞ uðkÞ ¼ Rðq1 Þ Rðq1 Þ Step 4
Repeat steps 1 and 3 at each sampling period
630
Chapter 14
and 1 Bm ðq1 Þ ¼ 1 þ 0:3q1 ¼ 2ð0:5 þ 0:15q1 Þ ¼ B ðq1 ÞBþ m ðq Þ
(a)
In the case of known plant parameters, calculate the pole-placement control law (b) In the case of unknown plant parameters, define an explicit adaptive poleplacement control scheme (c) Repeat part (b) for an implicit adaptive pole-placement control scheme. Solution (a) In the case of known parameters and to satisfy Eqs (14.4-23), (14.4-24), and (14.4-27) we select deg A0 ðq1 Þ ¼ 0, deg R1 ðq1 Þ ¼ 0, and deg Sðq1 Þ ¼ 1. Hence, A0 ðq1 Þ ¼ 1, R1 ðq1 Þ ¼ r0 , and Sðq1 Þ ¼ s0 þ s1 q1 . We now solve the following Diophantine equation for r0 , s0 , and s1 : Aðq1 ÞR1 ðq1 Þ þ q1 B ðq1 ÞSðq1 Þ ¼ Am ðq1 ÞA0 ðq1 Þ or ð1 þ 2q1 þ q2 Þr0 þ 2q1 ðs0 þ s1 q1 Þ ¼ 1 q1 þ 0:25q2 One easily obtains r0 ¼ 1, s0 ¼ 1:5, and s1 ¼ 0:375 and, consequently, R1 ðq1 Þ ¼ 1 and Sðq1 Þ ¼ 1:5 0:375q1 . Note from Eq. (14.4-14) that when A0 ðq1 Þ is a monic polynomial, then R1 ðq1 Þ and Rðq1 Þ are restricted to being monic polynoand mials also. Now, Rðq1 Þ ¼ Bþ ðq1 ÞR1 ðq1 Þ ¼ 1 þ 0:5q1 þ 0:5q2 1 þ 1 1 Tðq Þ ¼ Bm ðq ÞA0 ðq Þ ¼ 0:5 þ 0:15q1 . The control law is given by " # " # Tðq1 Þ Sðq1 Þ uðkÞ ¼ rðkÞ yðkÞ Rðq1 Þ Rðq1 Þ or "
# " # 0:5 þ 0:15q1 1:5 þ 0:375q1 uðkÞ ¼ rðkÞ þ yðkÞ 1 þ 0:5q1 þ 0:5q2 1 þ 0:5q1 þ 0:5q2 (b) The system model belongs to the following class of models: ð1 þ a1 q1 þ a2 q2 ÞyðkÞ ¼ ðb0 þ b1 q1 þ b2 q2 Þuðk 1Þ which may be rewritten as yðkÞ ¼ hT uðkÞ where hT ¼ ½a1 ; a2 ; b0 ; b1 ; b2 uT ðkÞ ¼ ½yðk 1Þ; yðk 2Þ; uðk 1Þ; uðk 2Þ; uðk 3Þ The parameters ai , bi can be estimated on-line using the following algorithm:
Adaptive Control
631
uT ðkÞ ¼ ½yðk 1Þ; yðk 2Þ; uðk 1Þ; uðk 2Þ; uðk 3Þ " # 1 FðkÞuðkÞuT ðkÞFðkÞ 1 FðkÞ Fðk þ 1Þ ¼ with Fð0Þ ¼ 3 I5 0:99 1 þ uT ðkÞFðkÞuðkÞ 10 "ðkÞ ¼ yðkÞ uT ðkÞh^ ðk 1Þ h^ ðkÞ ¼ h^ ðk 1Þ þ FðkÞuðkÞ"ðkÞ h i h^ T ðkÞ ¼ a^ 1 ðkÞ; a^2 ðkÞ; b^0 ðkÞ; b^1 ðkÞ; b^2 ðkÞ initialized for example at h^ T ð0Þ ¼ ½1; 0; 1; 0; 1 At each step B^ ðq1 Þ ¼ b^0 ðkÞ þ b^1 ðkÞq1 þ b^2 ðkÞq2 is factored as B^ ðq1 Þ ¼ B^ ðq1 ÞB^ þ ðq1 Þ where B^ þ ðq1 Þ is chosen to be monic. Moreover, at each step, the following Diophantine equation is solved for r^0 ðkÞ, s^0 ðkÞ, and s^1 ðkÞ:
A^ ðq1 Þ^r0 ðkÞ þ q1 B^ ðq1 Þ s^0 ðkÞ þ s^1 ðkÞq1 ¼ 1 q1 þ 0:25q2 where A^ ðq1 Þ ¼ 1 þ a^ 1 ðkÞq1 þ a^2 ðkÞq2
and
S^ ðq1 Þ ¼ s^0 ðkÞ þ s^1 ðkÞq1
Then, the following computations are made: R^ ðq1 Þ ¼ R^ 1 ðq1 ÞB^ þ ðq1 Þ ¼ r0 ðkÞB^ þ ðq1 Þ 1 Tðq1 Þ ¼ Bþ m ðq Þ
where 1 Bm ðq1 Þ ¼ B^ ðq1 ÞBþ m ðq Þ 1 Here, Bþ m ðq Þ can be any polynomial of our choice. The control law to be applied to the system at each step is " # " # Tðq1 Þ S^ ðq1 Þ rðkÞ yðkÞ uðkÞ ¼ R^ ðq1 Þ R^ ðq1 Þ
(c) In the case of an implicit adaptive pole-placement design, the parameters of the following implicit system model are estimated using recursive least squares: A0 ðq1 ÞAm ðq1 ÞyðkÞ ¼ q1 R ðq1 ÞuðkÞ þ q1 S ðq1 ÞyðkÞ or
ð1 q1 þ 0:25q2 ÞyðkÞ ¼ r0 ðkÞ þ r1 ðkÞq1 þ r2 ðkÞq2 uðk 1Þ
þ s0 ðkÞ þ s1 ðkÞq1 yðk 1Þ
632
Chapter 14
or
3 uðk 1Þ 6 uðk 2Þ 7 7 6 7 yðkÞ yðk 1Þ þ 0:25yðk 2Þ ¼ ½r0 ðkÞ; r1 ðkÞ; r2 ðkÞ; s0 ðkÞ; s1 ðkÞ6 6 uðk 3Þ 7 4 yðk 1Þ 5 yðk 2Þ 2
with R ðq1 Þ ¼ B ðq1 ÞRðq1 Þ and S ðq1 Þ ¼ B ðq1 ÞSðq1 Þ. Next, any common factors in R ðq1 Þ and S ðq1 Þ (corresponding to B ðq1 Þ) are canceled out to obtain Rðq1 Þ and Sðq1 Þ. One then has 1 Tðq1 Þ ¼ Bþ m ðq Þ
and the control law is given by " # " # Tðq1 Þ Sðq1 Þ rðkÞ yðkÞ uðkÞ ¼ Rðq1 Þ Rðq1 Þ The procedure described above is repeated at each sampling period. 14.5
PROBLEMS
1. Consider a system described by yðkÞ ¼ 0 Gðq1 ÞuðkÞ where 0 is an unknown parameter and Gðq1 Þ is a known rational function of q1 . The reference model is described by ym ðkÞ ¼ m Gðq1 ÞrðkÞ where m is a known parameter. The controller is of the form uðkÞ ¼ rðkÞ Find an adaptation mechanism for the feedforward gain , by using the MIT rule. 2. Consider a system described by " # q1 ð0:36 þ 0:28q1 Þ yðkÞ ¼ uðkÞ 1 1:36q1 þ 0:36q2 and a reference model given by " # q1 ð0:38 þ 0:24q1 Þ ym ðkÞ ¼ rðkÞ 1 0:78q1 þ 0:37q2 Determine an adaptive controller to achieve model following by using the MIT rule (assume that the parameters appearing in the system model are unknown). 3. Consider a system described by " # q2 0 yðkÞ ¼ uðkÞ 1 þ 1 q1 þ 2 q2
Adaptive Control
633
where 0 , 1 , and 2 are free parameters. The desired input–output behavior is given by b0 2 rðkÞ ym ðkÞ ¼ q 1 þ a1 q1 þ a2 q2 where 1 þ a1 q1 þ a2 q2 is an asymptotically stable polynomial. In the case where 0 , 1 , and 2 are assumed to be known, calculate a pole-placement control law (b) Design an implicit adaptive pole-placement algorithm in the case where 0 , 1 , and 2 are unknown. (a)
4. The plastic extrusion process is briefly described in Problem 18 (Sec. 12.14) of Chap. 12. The discrete-time system for the temperature control is shown in Figure 14.7. The transfer function from the screw speed (which is the main controlling variable) to the temperature of the polymer at the output is given by HðsÞ ¼
Kes s þ 1
where K is the static gain, is the time constant, and the system delay. The system delay is such that ¼ ðd 1ÞT þ L;
0
where T is the sampling period and d is a positive integer. (a)
Verify that the equivalent discrete-time transfer function, using a zeroorder hold circuit, is given by HðzÞ ¼
zd ðb0 þ b1 z1 Þ 1 þ a1 z1
with a1 ¼ eT= b0 ¼ K 1 eðLTÞ= b1 ¼ KeT= eL= 1
Figure 14.7
Temperature control system for plastic extrusion.
634
Chapter 14
The discretized system is thus equivalently described by the difference equation yðk þ dÞ ¼ a1 yðk þ d 1Þ þ b0 uðkÞ þ b1 uðk 1Þ (b)
It is clear that d is the discretized (sampled-date) system delay. Choose d ¼ 1 and a sampling period T such that the sampled-data system has a stable zero (i.e., choose b1 =b0 to be inside the unit circle). The minimum-phase system thus obtained is described by yðk þ 1Þ ¼ a1 yðkÞ þ b0 uðkÞ þ b1 uðk 1Þ For this system model, determine a model reference control law which, in the case of known model parameters, satisfies the control objective ðq1 Þ½yðk þ 1Þ ym ðk þ 1Þ 0
for
k0
where ðq1 Þ ¼ 1 þ 1 q1 . The polynomial ðq1 Þ is assumed to be asymptotically stable. The asymptotically stable reference model is given by " # m 1 q1 ðbm 0 þ b1 q Þ ym ðkÞ ¼ rðkÞ 1 1 þ am 1q (c)
In the case of unknown plant parameters, determine an MRAC design that satisfies the control objective of part (b) asymptotically.
5. A system is described by the model (initially known to the designer) [22] " # q2 ð1 þ 0:4q1 Þ yðkÞ ¼ uðkÞ ð1 0:5q1 Þ½1 ð0:8 þ 0:3jÞq1 ½1 ð0:8 0:3jÞq1 It is desired to follow the reference model " # q2 ð0:28 þ 0:22q1 Þ ym ðkÞ ¼ rðkÞ ð1 0:5q1 Þ½1 ð0:7 þ 0:2jÞq1 ½1 ð0:7 0:2jÞq1 By choosing ðq1 Þ ¼ 1, or ðq1 Þ ¼ ½1 0:4q1 3 , design a model reference control law, assuming the new model reference parameters known. (b) Suppose now that parameter changes occur in the system model. The system, after the parameter changes, is described by (the new model is assumed unknown to the designer) " # q2 ð0:9 þ 0:5q1 Þ uðkÞ yðkÞ ¼ ð1 0:5q1 Þ½1 ð0:9 þ 0:5jÞq1 ½1 ð0:9 0:5jÞq1 (a)
When the control objective is tracking, the changes occur at t ¼ 25 sec, while when the control objective is regulation, the changes occur at t ¼ 0 sec. In the case of unknown parameters determine an adaptive MRAC scheme. Simulate the behavior of the closed-loop system during tracking and during regulation, with the choices ðq1 Þ ¼ 1 or ðq1 Þ ¼ ½1 0:4q1 3 . Use as initial parameter values for the adaptive controller those obtained from the design in the nonadaptive case.
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635
Assume that a constant trace algorithm is used for the adaptation gain, with 1 ðkÞ=2 ðkÞ ¼ 1 and tr FðkÞ ¼ tr Fð0Þ, with Fð0Þ ¼ 10I. 6. For the system of Problem 4, it is desired to design an explicit STR, considering the model reference control as the ‘‘underlying control problem.’’ Define a leastsquares algorithm to estimate the plant parameters. Reparametrize the control law of Problem 4, so that the plant parameters appear explicitly. Solve the controller design problem and define the STR. Simulate the behavior of the system. 7. For the system of Problem 4, design an explicit pole-placement STR. Distinguish the cases of stable and unstable system zero. 8. Determine explicit and implicit self-tuners for the plant yðkÞ ¼ 0:86yðk 1Þ þ 0:08uðk 1Þ þ 0:06uðk 2Þ The desired characteristic polynomial is chosen as follows: Am ðq1 Þ ¼ 1 1:5q1 þ 0:6q2 1 Also, choose Bþ m ðq Þ ¼ 1. Use a forgetting adaptation algorithm with ¼ 0:95. Moreover, use Fð0Þ ¼ 100I. Simulate the behavior of the system.
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Chapter 14
Articles 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.
KJ A stro¨m. Theory and applications of adaptive control – a survey. Automatica 19(5):471–486, 1983. KJ A stro¨m, U Gorisson, L Ljung, B Wittenmark. Theory and applications of self-tuning regulators. Automatica 13:457–476, 1977. stro m, B Wittenmark. On self tuning regulators. Automatica 9:185–199, 1973. KJ A ¨ KJ A stro¨m, B Wittenmark. Self tuning controllers based on pole-zero placement. IEE Proc. 127 (Part D, No. 3):120–130, May 1980. T Ionescu, R Monopoli. Discrete model reference adapative control with an augmented error signal. Automatica 13:507–517, 1977. ID Landau. A survey of model reference adaptive techniques – theory and applications. Automatica 10:353–379, 1974. ID Landau. An extension of a stability theorem applicable to adaptive control. IEEE Trans Automatic Control AC-25(4):814–817, August 1980. ID Landau, R Lozano. Unification of discrete time explicit model reference adaptive control designs. Automatica 17(4):593–611, 1981. ID Landau, HM Silveiva. A stability theorem with applications to adaptive control. IEEE Trans Automatic Control AC-24(2):305–312, April 1979. R Lozano, ID Landau. Redesign of explicit and implicit discrete-time model reference adaptive control schemes. Int J Control 33(2):247–268, 1981. P Parks. Liapunov redesign of model reference adaptive control systems. IEEE Trans Automatic Control AC-11(3):362–367, July 1966. B Wittenmark. Stochastic adaptive control methods – a survey. Int J Control 21(5):705– 730, 1975. B Wittenmark, KJ A stro¨m. Practical issues in the implementation of self-tuning control. Automatica 20(5):595–605, 1984.
15 Robust Control
15.1
INTRODUCTION
Control engineers are always aware that any design of a controller based on a fixed plant model (e.g., transfer function or state space) is very often unrealistic. This is because there is always a nagging doubt about the performance specifications if the model on which the design is based deviates from the assumed value over a certain range. Robust control refers to the control of uncertain plants with unknown disturbance signals, uncertain dynamics, and imprecisely known parameters making use of fixed controllers. That is, the problem of robust control is to design a fixed controller that guarantees acceptable performance norms in the presence of plant and input uncertainty. The performance specification may include properties such as stability, disturbance attenuation, reference tracking, control energy reduction, etc. In the case of single-input–single-output (SISO) systems this is roughly covered by concepts such as gain and phase margins (see Chap. 8). However, in the multiple-input–multipleoutput (MIMO) systems case, matters become quite complicated and an easy extension of gain and phase margins is not possible. This led to new approaches and new techniques to deal with the situation. In this chapter, we report some of these new developments. We limit our presentation to linear time-invariant SISO systems for both the plant and the controller, wherein the controller configuration remains fixed. It was pointed out in Chap. 1 that control theory is a relatively new discipline and was recognized as such only during the early 1930s. The contributions made by Nyquist [27] and Bode [2] in those days, placed this discipline on firm theoretical foundations. Furthermore, the contribution of Wiener [10] and Kolmogorov [26] on filtering and prediction of stationary processes constitutes a landmark in stochastic control theory. In all these developments the emphasis was on frequency domain techniques. In particular, Bode and Nyquist understood and appreciated the concept of robustness, which is embodied in their definitions of gain and phase margins. An important change in control theory development took place in the 1950s with the emergence of the state-space approach. The high point of this development was reached during the 1960s with the formulation and solution of what is known as the linear quadratic gaussian (LQG) problem [5]. This development was primarily 637
638
Chapter 15
inspired by the important contributions of Kalman [21–24]. The LQG approach provided a mathematically elegant method for designing feedback controllers of systems working in a noisy environment. However, it was soon realized that one of its principal drawbacks was its inability to guarantee a robust solution. Control engineers found it difficult to incorporate robustness criteria in a quadratic integral performance index used in the LQG problem. For this reason, during the early 1970s, an attempt was made to generalize some of the useful concepts such as gain and phase margins so that they are made applicable to MIMO systems [7, 8]. This brings us to the 1980s, where a variety of ideas of far-reaching significance were developed in the area of robust control. Some of these ideas are the following: 1. 2. 3. 4. 5. 6.
7.
Use of singular values as a measure of gain in transformations [16] The factorization approach in controller synthesis [3, 9] Parametrization of stabilizing controllers [14, 28, 29] H1 optimization [5, 20, 31, 32] Robust stabilization and sensitivity minimization [12, 18, 25, 30] Computational aspects of H1 optimization, such as (a) Interpolation methods based on Nevanlinna–Pick interpolation theory [13] (b) Hankel norm approach [19] (c) Operator–theoretic approach [17] (d) State space approach using separation principle [15] Kharitonov theory and related approaches [1]
With all these developments, robust control gained a great momentum, and it is currently one of the most important areas of research in the field of control theory and practice. In the subsequent sections of this chapter, an introduction to the main problems and principles of robust control is presented based on certain transfer function approaches. There have been proposed many other important robust control techniques, which are not presented here due to space limitations. 15.2
MODEL UNCERTAINTY AND ITS REPRESENTATION
15.2.1
Origins of Model Uncertainty
Uncertainty in control systems may stem from different sources. Model uncertainty is one main consideration. Other considerations include sensor and actuator failures, physical constraints, changes in control purposes, loop opening and loop closure, etc. Moreover, in control design problems based on optimization, robustness issues due to mathematical objective functions not properly describing the real control problem may occur. On the other hand, numerical design algorithms may not be robust. However, when we refer to robustness in this chapter, we mean robustness with respect to model uncertainty. We also assume that a fixed linear controller is used. Model uncertainty may have several origins. In particular, it may be caused by: 1. 2.
Parameters in a linear model, which are approximately known or are simply in error Parameters, which may vary due to nonlinearities or changes in the operating conditions
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3. 4. 5. 6. 7.
639
Neglected time delays and diffusion processes Imperfect measurement devices Reduced (low-order) models of a plant, which are commonly used in practice, instead of very detailed models of higher order Ignorance of the structure and the model order at high frequencies Controller order reduction issues and implementation inaccuracies.
The above sources of model uncertainty may be grouped into three main categories. Parametric or Structured Uncertainty In this case the structure of the model and its order is known, but some of the parameters are uncertain and vary in a subset of the parameter space. Neglected and Unmodeled Dynamics Uncertainty In this case the model is in error because of missing dynamics (usually at high frequencies), most likely due to a lack of understanding of the physical process. Lumped Uncertainty or Unstructured Uncertainty In this case uncertainty represents several sources of parametric and/or unmodeled dynamics uncertainty combined into a single lumped perturbation of prespecified structure. Here, nothing is known about the exact nature of the uncertainties, except that they are bounded. 15.2.2 Representation of Uncertainty Parametric uncertainty will be quantified by assuming that each uncertain parameter is bounded within some region ½min ; max . In other words, there are parameter sets of the form p ¼ m ð1 þ r Þ
ð15:2-1Þ
where m is the mean parameter value, r ¼ ðmax min Þ=ðmax þ min Þ is the relative parametric uncertainty and is any scalar satisfying jj 1. Neglected and unmodeled dynamics uncertainty is more difficult to quantify. The frequency domain is particularly well suited for representing this class of uncertainty, through complex perturbations, which are normalized such that kk1 1, where kk1 is given by the following definition. Definition 15.2.1 For a scalar complex function ðsÞ, the H1 -norm of ðsÞ is defined as kk1 ¼ sup jð j!Þj !
Lumped unstructured uncertainty can easily be described in the frequency domain. In most cases, it is preferred to lump the uncertainty into a ‘‘multiplicative uncertainty’’ of the form P m : Gp ðsÞ ¼ GðsÞ½1 þ wm ðsÞm ðsÞ; where
km k1 1
ð15:2-3Þ
640
Chapter 15
P m : set of possible linear time-invariant models (usually called the uncertainty set) GðsÞ 2 P m : nominal plant model (without uncertainty) Gp ðsÞ 2 P m : perturbed plant model wm ðsÞ : the multiplicative uncertainty weight m ðsÞ : any stable transfer function which is less than or equal to 1 in magnitude at all frequencies. Some examples of allowable m ðsÞ with kk1 1 are 1 s þ 1
and
0:1 ; s þ 0:1s þ 1
where
2
>0
It is worth noting at this point that the requirement for stability on m ðsÞ may be removed if one assumes that the number of right-half plane poles in GðsÞ and Gp ðsÞ remains unchanged. The block diagram of Figure 15.1 represents a plant with multiplicative uncertainty. Other less common uncertainty forms are the additive uncertainty, having the form P a : Gp ðsÞ ¼ GðsÞ þ wa ðsÞa ðsÞ;
ka k1 1
ð15:2-4Þ
the inverse multiplicative uncertainty, having the form P im : Gp ðsÞ ¼ GðsÞ½1 þ wim ðsÞim ðsÞ1 ;
kim k1 1
ð15:2-5Þ
kd k1 1
ð15:2-6Þ
and the division uncertainty, having the form P d : Gp ðsÞ ¼ GðsÞ½1 þ wd ðsÞd ðsÞGðsÞ1 ;
It is pointed out here that the additive and the multiplicative uncertainty descriptions are equivalent if at each frequency the following relation holds: jwm ð j!Þj ¼
jwa ð j!Þj jGð j!Þj
ð15:2-7Þ
Next, we give some examples on representing parametric model uncertainty. Example 15.2.1 Assume that the set of possible plants is given by
Figure 15.1
Plant with multiplicative uncertainty.
Robust Control
GP ðsÞ ¼ p G0 ðsÞ;
641
min P max
ð15:2-8Þ
where p is an uncertain gain and G0 ðsÞ is a transfer function without uncertainty. Determine the multiplicative uncertainty description of Eq. (15.2-8). Solution Write p as follows p ¼ m ð1 þ r Þ;
jj 1
where m ¼
min þ max 2
r ¼
and
max min max þ min
Clearly, m and r are the average gain and the relative magnitude of the gain uncertainty, respectively. Therefore, the model set (15.2-8) can be written as GP ðsÞ ¼ m G0 ðsÞ½1 þ r ¼ GðsÞ½1 þ r ;
kk 1
The above expression for Gp ðsÞ is the multiplicative uncertainty description of the set (15.2-8). Example 15.2.2 Assume that the set of possible plants is given by GP ðsÞ ¼
1 G ðsÞ; P s þ 1 0
min p max
ð15:2-9Þ
Determine the inverse multiplicative uncertainty description of Eq. (15.2-9). Solution By writing P ¼ m ð1 þ r Þ;
jj 1
where m ¼
min þ max 2
and
r ¼
max min max þ min
the model set (15.2-9) can be written as Gp ðsÞ ¼
G0 ðsÞ G ðsÞ ¼ 0 ½1 þ wim ðsÞ1 ¼ GðsÞ½1 þ wim ðsÞ1 1 þ m s þ r m s 1 þ m s
where GðsÞ ¼
G0 ðsÞ 1 þ rm s
and
wim ðsÞ ¼
r m s 1 þ m s
The above expression for Gp ðsÞ is the inverse multiplicative form of the uncertainty set (15.2-9). Example 15.2.3 Assume that the set of possible plants is given by
642
Chapter 15
Gp ðsÞ ¼ ð1 þ zp sÞG0 ðsÞ;
zmin zp zmax
ð15:2-10Þ
where G0 ðsÞ is assumed to have no uncertainty. Determine the multiplicative uncertainty form of Eq. (15.2-10). Solution By writing zp ¼ zm ð1 þ rz Þ;
jj 1
where zm ¼
zmax þ zmin 2
rz ¼
and
zmax zmin zmax þ zmin
the model (15.2-10) can be written as Gp ðsÞ ¼ ð1 þ zm s þ zm rz sÞG0 ðsÞ ¼ ð1 þ zm sÞG0 ðsÞ þ zm rz sG0 ðsÞ z rs z rs ¼ ð1 þ zm sÞG0 ðsÞ þ m z ð1 þ zm sÞG0 ðsÞ ¼ GðsÞ þ m z GðsÞ 1 þ zm s 1 þ zm s ¼ GðsÞ½1 þ wm ðsÞ where GðsÞ ¼ ð1 þ zm sÞG0 ðsÞ
and
wm ðsÞ ¼
zm r z s 1 þ zm s
The above expression for Gp ðsÞ is the multiplicative form of the uncertainty set (15.210). Example 15.2.4 Consider the family of plant transfer functions Gp ðsÞ ¼
1 ; s2 þ s þ 1
0:4 0:8
ð15:2-11Þ
Determine the division uncertainty description of Eq. (15.2-11). Solution It is easy to see that ¼ 0:6 þ 0:2;
jj 1
Therefore, the model set (15.2-11) can be expressed as Gp ðsÞ ¼ GðsÞ½1 þ wd ðsÞGðsÞ1 where GðsÞ ¼
1 ; s2 þ 0:6s þ 1
wd ðsÞ ¼ 0:2s
The foregoing relation for Gp ðsÞ is the division uncertainty description of the family (15.2-11). Although parametric uncertainty is easily represented in some simple cases, it is avoided in most cases because of the following reasons:
Robust Control
1. 2.
3.
643
It requires large efforts to model parametric uncertainty, particularly in the case of systems with a large number of uncertain parameters. In many cases, the assumptions about the model and the parameters may be inexact. However, the description of a family of systems through parametric uncertainty is very detailed and accurate. In order to model uncertain systems through parametric uncertainty, the exact model structure is indispensable. Unmodeled dynamics cannot then be incorporated in this description.
We next focus our attention on the problem of describing a set of possible plants P by a single unstructured perturbation a ðsÞ or m ðsÞ. This description can be obtained on the basis of the following steps: 1.
2.
Choose a nominal model GðsÞ. A nominal model can be selected to be either a low-order, delay-free model or a model of mean parameter values or, finally, the central plant obtained from the Nyquist plots corresponding to all of the plants of the given set P . In the case of additive uncertainty, find the smallest radius ‘a ð!Þ, which includes all possible plants ‘a ð!Þ ¼ max jGp ð j!Þ Gð j!Þj
ð15:2-12Þ
Gp 2P a
In most cases we look for a rational transfer function weight wa ðsÞ for additive uncertainty. This weight must be chosen such that jwa ð j!Þj ‘a ð!Þ;
3.
8!
ð15:2-13Þ
and is usually selected to be of low order to simplify the design of controllers. In the case of multiplicative uncertainty (which is the preferred uncertainty form), find the smallest radius ‘m ð!Þ, which includes all possible plants Gp ð j!Þ Gð j!Þ ð15:2-14Þ ‘m ð!Þ ¼ max Gp 2P m Gð j!Þ For a chosen rational weight wm ðsÞ, there must be jwm ð j!Þj ‘m ð!Þ;
8!
ð15:2-15Þ
We next give an example of how this approach is applied in practice. Example 15.2.5 Consider the family of plants with parametric uncertainty given by P : Gp ðsÞ ¼
s ; s2 þ as þ b
1 a 3;
2b6
ð15:2-16Þ
Obtain a representation of the above set using multiplicative uncertainty with a single rational weight wp ðsÞ. Solution Choose a nominal model as the model of mean parameter values. That is, let
644
Chapter 15
GðsÞ ¼
s s2 þ 2s þ 4
In order to obtain ‘m ð!Þ, we consider three values for each of the two parameters a and b. In particular, we consider a ¼ 1, 2.5, or 3 and b ¼ 2, 5, or 6. With this choice, we obtain 32 ¼ 9 alternative plants. In general, this choice does not guarantee that the worst case is obtained, since the worst case may correspond to another interior point of the intervals. However, in our example, it can be shown that the worst case corresponds to the choice a ¼ 1 and b ¼ 2. The relative error Gp ð j!Þ Gð j!Þ Gð j!Þ for the nine resulting Gp ðsÞ, are depicted as functions of frequency in Figure 15.2. According to our analysis, the curve for ‘m ð!Þ must at each frequency lie above all the curves corresponding to the nine Gp ðsÞ. One can also observe that as s ! 0, ‘m ð!Þ ! 1, and as s ! 1, ‘m ð!Þ ! 0. With these observations one must choose a simple first-order weight that approximately matches this limiting behavior, as for example wm ðsÞ ¼
20 s þ 20
ð15:2-17Þ
This weight is also depicted in Figure 15.2. It can be shown that this weight gives a good fit of ‘m ð!Þ, except at frequencies around ! ¼ 1:5, where jwm ð j!Þj is too small, and thus this weight does not include all possible plants. To change this such that jwm ð j!Þj ‘m ð!Þ, 8!, we can work along two alternative approaches: The first approach is to augment the numerator of Eq. (15.2-17), as for example wm ðsÞ ¼
Figure 15.2
37 s þ 20
ð15:2-18Þ
The first-order multiplicative uncertainty weight (15.2-17) for Example 15.2.5.
Robust Control
Figure 15.3
645
The first-order multiplicative uncertainty weight (15.2-18) for Example 15.2.5.
This multiplicative uncertainty weight is shown in Figure 15.3 and obviously includes all possible plants. The second approach is to multiply wm ðsÞ of the form (15.2-17) by a correction factor to lift the gain slightly at ! ¼ 1:5. Thus, for example, we obtain the following weight: wm ðsÞ ¼
20ð2:15s þ 1Þ 43s þ 20 ¼ ðs þ 20Þðs þ 1Þ s2 þ 21s þ 20
ð15:2-19Þ
This second-order multiplicative uncertainty weight is shown in Figure 15.4, and obviously includes all possible plants.
15.3
ROBUST STABILITY IN THE H1 -CONTEXT
In the previous section we discussed how to represent model uncertainty in a mathematical context. In this section, we will derive conditions under which a system remains stable for all perturbations in an uncertainty set.
15.3.1 Robust Stability with a Multiplicative Uncertainty In Figure 15.5, a feedback system with a plant HðsÞ, a controller KðsÞ, and a multiplicative uncertainty is presented. In what follows, our aim is to determine whether the stability of the uncertain feedback system is maintained, if there is a multiplicative uncertainty of magnitude jwm ð j!Þj. With the uncertainty present, the open-loop transfer function of the feedback system of Figure 15.5 is given by
646
Chapter 15
Figure 15.4
The second-order multiplicative uncertainty weight (15.2-19) for Example
15.2.5.
Gp ðsÞ ¼ Hp ðsÞKðsÞ ¼ HðsÞKðsÞ½1 þ wm ðsÞm ðsÞ ¼ GðsÞ þ wm ðsÞGðsÞm ðsÞ; km ð j!Þk1 1 ð15:3-1Þ Assume that, by design, the stability of the nominal closed-loop system is guaranteed. For simplicity, we also assume tht the open-loop transfer function Gp ðsÞ is stable. To test for robust stability of the closed-loop feedback system, we use the Nyquist stability condition. Then, we obtain that robust stability, which is equivalent to the stability of the system for all Gp ðsÞ, is also equivalent to the fact that Gp ðsÞ should not encircle the point 1 þ j0, for all Gp ðsÞ. Now, consider a typical plot of Gp ðsÞ as shown in Figure 15.6. The distance from the point 1 þ j0 to the center of the disk, which represents Gp ðsÞ, is j1 þ GðsÞj.
Figure 15.5
Closed-loop feedback system with multiplicative uncertainty.
Robust Control
Figure 15.6
647
Graphical derivation of the robust stability condition through Nyquist plot.
Furthermore, the radius of this disk is jwm ðsÞGðsÞj. To avoid encirclement of 1 þ j0, none of the disks should cover the critical point. By inspection of Figure 15.6, we conclude that the encirclement is avoided if and only if jwm ðsÞGðsÞj < j1 þ GðsÞj; 8!
ð15:3-2Þ
or equivalently if and only if wm ðsÞGðsÞ 1 þ GðsÞ < 1; 8!
ð15:3-3Þ
Definition 15.3.1 The sensitivity function designated by SðsÞ and the complementary sensitivity function designated by TðsÞ are defined as follows: SðsÞ ¼ ½1 þ GðsÞ1 ¼ ½1 þ KðsÞHðsÞ1 TðsÞ ¼ KðsÞHðsÞ½1 þ KðsÞGðsÞ1
and
ð15:3-4Þ
The sensitivity functions SðsÞ and TðsÞ satisfy the relation SðsÞ þ TðsÞ ¼ 1. Using Eq. (15.3-4), one can conclude that the encirclement is avoided (equivalently the robust stability condition is satisfied) if and only if jwm ðsÞTðsÞj < 1; 8!
ð15:3-5Þ
Making use of Definition 15.2.1, we can finally conclude that robust stability under multiplicative perturbation is assumed if and only if kwm ðsÞTðsÞk1 < 1
ð15:3-6Þ
It is worth noting that the robust stability condition (15.3-6) for the case of the multiplicative uncertainty gives an upper bound on the complementary sensitivity function. In other words, to guarantee robust stability in the case of multiplicative uncertainty one has to make TðsÞ small at frequencies where the uncertainty weight exceeds 1 in magnitude.
648
Chapter 15
Condition (15.3-6) is necessary and sufficient provided that, at each frequency, all perturbations satisfying jm ð j!Þj 1 are possible for the feedback system studied. If this is not the case, the condition is only sufficient. An alternative, rather algebraic, way of obtaining the robust stability condition (15.3-6) is the following. Since Gp ðsÞ is assumed to be stable and the nominal closedloop system is stable by design, then the nominal open-loop transfer function does not encircle the critical point 1 þ j0. Conseqently, since the family of uncertain plants is norm bounded, it then follows that, if for some Gp1 ðsÞ in the uncertain family, we have encirclement of 1 þ j0, then there must be another Gp2 ðsÞ in the uncertain family, which passes through 1 þ j0 at some frequency. Therefore, to guarantee robust stability, the following condition must hold (and vice versa): j1 þ Gp ðsÞj 6¼ 0; 8Gp ; 8!
ð15:3-7Þ
Hence, robust stability is guaranteed if and only if j1 þ GðsÞ þ wm ðsÞGðsÞm ðsÞj > 0; 8jm ðsÞj 1; 8!
ð15:3-8Þ
This last condition is most easily violated at each frequency when m ð j!Þ has magnitude 1 and the phase is such that the terms 1 þ GðsÞ and wm ðsÞGðsÞm ðsÞ have opposite signs. Thus, robust stability is guaranteed if and only if j1 þ GðsÞj jwm ðsÞGðsÞj > 0; 8!
ð15:3-9Þ
Then, condition (15.3-6) follows easily. We next give an example of how to check robust stability when using multiplicative perturbation. Example 15.3.1 Consider the uncertain feedback control system of Figure 15.5. Assume that the uncertain plant transfer function is given by Hp ðsÞ ¼ HðsÞ½1 þ wm ðsÞm ðsÞ where HðsÞ ¼
1 s1
and
wm ðsÞ ¼
2 s þ 10
while the controller KðsÞ is a constant gain controller of the form KðsÞ ¼ 10. Determine whether the closed-loop system is robustly stable. Solution For this case the complementary sensitivity function TðsÞ is given by TðsÞ ¼
10 sþ9
Figure 15.7 gives the magnitude of TðsÞ as a function of the frequency, versus the magnitude of 1=wm ðsÞ ¼ ðs þ 10Þ=2. From the figure, it is clear that, at each frequency, the magnitude of 1=wm ðsÞ overbounds the magnitude of TðsÞ. Hence, in our case, condition (15.3-6) is satisfied, and the closed-loop system is robustly stable.
Robust Control
Figure 15.7
649
Checking robust stability with a multiplicative uncertainty, for Example 15.3.1.
15.3.2 Robust Stability with an Inverse Multiplicative Uncertainty In this subsection a corresponding robust stability condition is derived for the case of feedback systems with inverse multiplicative uncertainty. To this end, we consider the feedback system of Figure 15.8, with a plant HðsÞ, a controller KðsÞ, and an inverse multiplicative uncertainty of magnitude wim ðsÞ. That is, here, Hp ðsÞ ¼ HðsÞ½1 þ wim ðsÞim ðsÞ1
ð15:3-10Þ
Now, suppose that the open-loop transfer function Gp ðsÞ is stable and that the nominal closed-loop system is also stable. As mentioned above, robust stability is guaranteed, if encirclements of the point 1 þ j0 are avoided, and since Gp ðsÞ belongs to a norm-bounded set, we conclude that robust staiblity is guaranteed if and only if one of the following four equivalent inequalities holds: j1 þ Gp ðsÞj > 0; 8Gp ðsÞ; 8!
ð15:3-11aÞ
j1 þ GðsÞ½1 þ wim ðsÞim ðsÞ1 j > 0; 8jim ð j!Þj 1; 8!
ð15:3-11bÞ
Figure 15.8
Closed-loop feedback system with inverse multiplicative uncertainty.
650
Chapter 15
1 þ GðsÞ þ wim ðsÞim ðsÞ 1 þ w ðsÞ ðsÞ > 0; 8jim ð j!Þj 1; 8! im im
ð15:3-11cÞ
j1 þ GðsÞ þ wim ðsÞim ðsÞj > 0; 8jim ð j!Þj 1; 8!
ð15:3-11dÞ
The last condition is most easily violated at each frequency when im ð j!Þ has magnitude 1 and the phase is such that the terms 1 þ GðsÞ and wim ðsÞim ðsÞ have opposite signs. Thus, robust stability is guaranteed if and only if j1 þ GðsÞj jwim ðsÞj > 0; 8!
ð15:3-12Þ
Taking into account the definitions of the sensitivity function SðsÞ and of the H1 -norm, we finally obtain that robust stability with inverse multiplicative uncertainty is guaranteed if and only if kwim ðsÞSðsÞk1 < 1
ð15:3-13Þ
Condition (15.3-13) indicates that in order to guarantee robust stability, in the case of an inverse multiplicative perturbation, one has to make SðsÞ small at frequencies where the uncertainty weight exceeds 1 in magnitude. Example 15.3.2 Consider the feedback system of Figure 15.8. Assume that the uncertain plant transfer function is given by Hp ðsÞ ¼ HðsÞ½1 þ wim ðsÞim ðsÞ1 where HðsÞ ¼
1 s1
and
wim ðsÞ ¼
s þ 2:1 3s þ 0:7
while the controller KðsÞ is a PI controller of the form KðsÞ ¼ 1 þ
2 s
Determine whether the closed-loop system is robustlys table. Solution For this case, the sensitivity function SðsÞ is given by SðsÞ ¼
s2 s s2 þ 2
Figure 15.9 gives the magnitude of SðsÞ as a function of the frequency versus the magnitude of 1=wim ðsÞ ¼ ð3s þ 0:7Þ=ðs þ 2:1Þ. From the figure, it is clear that, at each frequency, the magnitude of 1=wim ðsÞ overbounds the magnitude of SðsÞ. Hence, in our case, condition (15.3-4) is satisfied and the closed-loop system is robustly stable. Remark 15.3.1 In the case of other well-known uncertainty descriptions, such as the additive or the division uncertainty, one can easily obtain robust stability conditions analogous to the conditions (15.3-6) and (15.3-13). Table 15.1 summarizes the robust stability tests for several commonly used uncertainty models.
Robust Control
651
Figure 15.9 Checking robust stability with an inverse multiplicative uncertainty, for Example 15.3.2.
15.4
ROBUST PERFORMANCE IN THE H1 -CONTEXT
In this section we study the performance of a perturbed plant. The general notion of robust performance is that internal stability and performance, of a specific type, should hold for all plants in a family P . Before dealing with robust performance, we study briefly the nominal performance and its relation to the sensitivity function.
15.4.1 Nominal Performance Consider the feedback system presented in Figure 15.10. Here, HðsÞ is the (unperturbed) plant transfer function, KðsÞ is the controller transfer function, rðtÞ or RðsÞ is the reference input (command, setpoint), dðtÞ or DðsÞ is the disturbance (process noise), nðtÞ or NðsÞ is the measurement noise, ym ðtÞ or Ym ðsÞ is the measured output,
Table 15.1
Robust Stability Tests
Uncertainty description
Robust stability condition
Additive uncertainty GðsÞ þ wa ðsÞa ðsÞ
kwa ðsÞKðsÞSðsÞk1 < 1
Multiplicative uncertainty GðsÞð1 þ wm ðsÞm ðsÞÞ
kwm ðsÞTðsÞk1 < 1
Inverse multiplicative uncertainty GðsÞð1 þ wim ðsÞim ðsÞÞ1
kwim ðsÞSðsÞk1 < 1
Division uncertainty GðsÞð1 þ wd ðsÞGðsÞd ðsÞÞ1
kwd ðsÞGðsÞSðsÞk1 < 1
652
Chapter 15
Figure 15.10
Block diagram of feedback control system with disturbance and noise.
and uðtÞ or UðsÞ is the control signal (actuator signal). The control error eðtÞ ¼ yðtÞ rðtÞ or EðsÞ ¼ YðsÞ RðsÞ is given by EðsÞ ¼ ½1 þ KðsÞHðsÞ1 RðsÞ þ ½1 þ KðsÞHðsÞ1 Gd ðsÞDðsÞ KðsÞHðsÞ½1 þ KðsÞHðsÞ1 NðsÞ
ð15:4-1Þ
or, in terms of the sensitivity and the complementary sensitivity functions, EðsÞ ¼ SðsÞRðsÞ þ SðsÞGd ðsÞDðsÞ TðsÞNðsÞ
ð15:4-2Þ
For ‘‘perfect control,’’ we want eðtÞ ¼ yðtÞ rðtÞ ¼ 0. That is, we would like to have good disturbance rejection and command tracking as well as reduction of measurement noise on the plant output. This means that, for disturbance rejection and command tracking, the sensitivity function SðsÞ must be chosen to be small in magnitude, whereas for zero noise transmission the same function must have a large magnitude, close to 1 (in this case TðsÞ is small in magnitude). This illustrates the fundamental nature of feedback design, which always involves a trade-off among conflicting control objectives. Moreover, it illustrates that the sensitivity function SðsÞ is a very good indicator of closed-loop performance. In particular, when considering SðsÞ as such an indicator, our main advantage stems from the fact that it is sufficient to consider just its magnitude and not worry about its phase. Some very common specifications in terms of SðsÞ are listed below: 1. 2. 3. 4.
Maximum tracking error at prespecified frequencies Minimum steady-state tracking error A Maximum peak magnitude M of SðsÞ Minimum bandwidth ! B
Performance specifications of the above type can usually be incorporated in an upper bound, 1=jwP ðsÞj, on the magnitude of the sensitivity function, where wP ðsÞ is a weight chosen by the designer. The subscript P stands for performance, since, as already mentioned, the sensitivity function is used as a performance indicator. Then, the performance requirement is guaranteed if and only if one of the following three equivalent inequalities holds:
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653
jSð j!Þj < 1=jwP ð j!Þj; 8!
ð15:4-3aÞ
jwP ð j!ÞSð j!Þj < 1; 8!
ð15:4-3bÞ
kwP ðsÞSðsÞk1 < 1
ð15:4-3cÞ
A typical performance weight is the following: wP ðsÞ ¼
s=M þ ! B s þ ! B A
ð15:5-4Þ
It can be easily seen from Eq. (15.4-4) that 1. 2. 3.
As s ! 0, SðsÞ ! A: As s ! 1, SðsÞ ! M: The asymptote of the Bode plot of the magnitude of SðsÞ crosses 0 dB, at the frequency ! B , which is the bandwidth requirement.
Now, consider the Nyquist plot of Figure 15.11. Taking into account the definition of the sensitivity function, one can obtain from Eq. (15.4-3) that nominal performance is equivalent to jwP ð j!Þj < j1 þ Gð j!ÞÞj; 8!
ð15:4-5Þ
At each frequency, the term j1 þ GðsÞj is the distance of GðsÞ from the critical point 1 þ j0 in the Nyquist plot. Therefore, for nominal performance, Gð j!Þ must be at least at a distance of jwP ð j!Þj from the critical point. In other words, for nominal performance, Gð j!Þ must stay outside a disk of radius jwP ð j!Þj, centered at 1 þ j0. This graphical interpretation of nominal performance is depicted in Figure 15.11. Example 15.4.1 Consider the feedback system depicted in Figure 15.10, with HðsÞ ¼
1 s1
and
KðsÞ ¼ 10
Let the design specifications for the closed-loop system be the following: 1.
Steady-state tracking error A ¼ 0:2
Figure 15.11
Nominal performance in the Nyquist plot.
654
Chapter 15
2. 3.
Maximum peak magnitude M ¼ 2 of the sensitivity function SðsÞ Minimum bandwidth ! B ¼ 0:5 rad/sec
Determine whether the closed-loop system meets the nominal performance requirement. Solution These design specifications can be written in the form of a rational performance bound wP ðsÞ of the form (15.4-4). In particular, for the present case, the performance bound has the form wP ðsÞ ¼
sþ1 2s þ 0:2
In Figure 15.12, the magnitude of the sensitivity function SðsÞ as a function of the frequency, versus the magnitude of 1=wP ðsÞ ¼ ðs þ 1Þ=ð2s þ 0:2Þ is shown. From the figure it is apparent that, at each frequency, the magnitude of the sensitivity function SðsÞ is bounded by the magnitude of 1=wP ðsÞ. Therefore, in our case, condition (15.4-3) is satisfied, and the closed-loop system meets the nominal performance requirement. 15.4.2
Robust Performance
Clearly, for robust performance, it is sufficient to require that condition (15.4-5) is satisfied for all possible plants Gp ðsÞ. In mathematical terms, robust performance is defined by one of the following two equivalent inequalities: jwP ð j!ÞSp ð j!Þj < 1; 8Sp ; 8!
ð15:4-6aÞ
jwP ð j!Þj < j1 þ Gp ð j!Þj; 8Gp ; 8!
ð15:4-6bÞ
Figure 15.12
Checking nominal performance for Example 15.4.1.
Robust Control
Figure 15.13
655
Block diagram for robust performance in the multiplicative uncertainty case.
Next, and for sake of simplicity, we focus our attention on the multiplicative uncertainty case. Figure 15.13 presents the block diagram for robust performance in the multiplicative uncertainty case. It is not difficult to see that condition (15.4-6) corresponds to the requirement j y =dj < 1; 8m . In this case, the set of possible open-loop transfer functions is given by Gp ðsÞ ¼ KðsÞHp ðsÞ ¼ GðsÞ½1 þ wm ðsÞm ðsÞ ¼ GðsÞ þ wm ðsÞGðsÞm ðsÞ
ð15:4-7Þ
Now, consider the Nyquist plot of Figure 15.14. To guarantee robust performance, one must require that all possible open-loop transfer functions Gp ð j!Þ stay outside a disk of radius jwP ð j!Þj centered on the critical point 1 þ j0. It is evident that Gp ð j!Þ, at each frequency, stays within a disk of radius wm ð j!ÞGð j!Þ, centered on Gð j!Þ. Therefore, from Figure 15.14, the condition for robust performance is that these two disks must not overlap. The centers of the two disks are located at a distance j1 þ Gð j!Þj apart. Consequently, the robust performance is guaranteed if and only if one of the following two equivalent inequalities holds: jwP ð j!Þj þ jwm ð j!ÞGð j!Þj < j1 þ Gð j!Þj; 8!
ð15:4-8aÞ
jwP ð j!Þ½1 þ Gð j!Þ1 j þ jwm ð j!ÞGð j!Þ½1 þ Gð j!Þ1 j < 1; 8!
ð15:4-8bÞ
Taking into account the definitions of the sensitivity and the complementary sensitivity functions, we may further obtain robust performance if and only if
Figure 15.14
Robust performance in the Nyquist plot.
656
Chapter 15
jwP ð j!ÞSð j!Þj þ jwm ð j!ÞTð j!Þj < 1; 8! or in other words kjwP ð j!ÞSð j!Þj þ jwm ð j!ÞTð j!Þjk1 < 1
ð15:4-9Þ
where, in deriving Eq. (15.4-9), use was made of the definition of the H1 -norm. Relation (15.4-9) is a necessary and sufficient condition for robust performance. An alternative (rather algebraic) way of obtaining the robust performance condition (15.4-9) is the following. According to relation (15.4-6a), robust performance is guaranteed if the maximum weighted sensitivity wP ðsÞSðsÞ, at each frequency, is less than 1 in magnitude. This means that robust performance is assured if and only if sup jwP ð j!ÞSp ð j!Þj < 1; 8!
ð15:4-10Þ
Sp
The perturbed sensitivity is Sp ðsÞ ¼ ½1 þ Gp ðsÞ1 ¼
1 1 þ GðsÞ þ wm ðsÞGðsÞðsÞ
ð15:4-11Þ
The worst-case (maximum) is obtained in the case where, at each frequency, we select jm ðsÞj ¼ 1, such that the signs of the terms 1 þ GðsÞ and wm ðsÞGðsÞm ðsÞ are opposite. In mathematical terms, we have sup jwP ð j!ÞSp ð j!Þj ¼ Sp
jwP ð j!Þj jwP ð j!ÞSð j!Þj ¼ j1 þ Gð j!Þj jwm ð j!ÞGð j!Þj 1 jwm ð j!ÞTð j!Þj ð15:4-12Þ
Combining relations (15.4-10) and (15.4-12) and taking into account the definition of the H1 -norm we readily obtain the robust performance condition (15.4-9). Condition (15.4-9) provides us with some useful bounds on the magnitude of GðsÞ. In particular, by observing that j1 þ Gð j!Þj 1 jGð j!Þj and j1 þ Gð j!Þj jGð j!Þj 1, 8!, we can easily see that the robust performance condition (15.4-9) is satisfied if jGð j!Þj >
1 þ jwP ð j!Þj ; 8! : jwm ð j!Þj < 1 1 jwm ð j!Þj
ð15:4-13aÞ
jGð j!Þj <
1 jwP ð j!Þj ; 8! : jwP ð j!Þj < 1 1 þ jwm ð j!Þj
ð15:4-13bÞ
or if
We can also prove that the robust performance condition is satisfied if jGð j!Þj >
jwP ð j!Þj 1 ; 1 jwm ð j!Þj
8! : jwm ð j!Þj < 1
and
jwP ð j!Þj > 1 ð15:4-14aÞ
or if jGð j!Þj <
1 jwP ð j!Þj ; jwm ð j!Þj 1
8! : jwP ð j!Þj < 1;
and
jwm ð j!Þj > 1 ð15:4-14bÞ
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657
For the case of SISO systems the term jwP ðsÞSðsÞj þ jwm ðsÞTðsÞj is the structured singular value (SSV) designated by ð!Þ. With this definition, robust performance is guaranteed if and only if k ð!k1 < 1
ð15:4-15Þ
Example 15.4.2 Consider the feedback control system of Figure 15.13, where HðsÞ ¼
1 ; KðsÞ ¼ 10; s1
wm ðsÞ ¼
2 ; s þ 10
and
wP ðsÞ ¼
sþ1 2s þ 0:2
As shown in Example 15.3.1, the system is robustly stable. Moreover, in Example 15.4.1, it has been shown that the nominal plant satisfies the nominal performance specification imposed by wP ðsÞ. Determine whether the closed-loop system also satisfies the robust performance specification. Solution Figure 15.15 shows that the structured singular value ð!Þ as function of the frequency. From this figure, it becomes clear that, at each frequency, the SSV is less than 1. Therefore, condition (15.4-15) (or equivalently, condition (15.4-9)) is satisfied, and robust performance of the feedback system is guaranteed. 15.4.3 Some Remarks on Nominal Performance, Robust Stability, and Robust Performance Consider once again the block diagram of Figure 15.13 representing a feedback loop with multiplicative uncertainty and assume that the nominal closed-loop system is stable. Relations (15.4-3), (15.3-5), and (15.4-9) (or (15.4-15)) give the conditions for nominal performance, robust stability, and robust performance, respectively. From
Figure 15.15
Checking robust performance for Example 15.4.2.
658
Chapter 15
these conditions, it becomes clear that the closed-loop system satisfies the robust performance specification if it satisfies the nominal performance specification and is simultaneously robustly stable. Therefore, robust performance is not our primary objective for siso systems. Our primary objectives for siso systems are nominal performance and robust stability. However, this is not true, in general, for the MIMO system case. From condition (15.3-5), it is clear that in order to satisfy robust stability we want, in general, to make TðsÞ small. On the other hand, for nominal performance, we want, in general, to make SðsÞ small. However, since SðsÞ þ TðsÞ ¼ 1, we cannot make both S(s) and T(s) small at the same frequency. That is, we cannot satisfy more than 100% uncertainty and good performance at the same frequency. This is another example of conflicting control objectives in feedback control systems. It is worth noting at this point that robust performance can be viewed as a special case of robust stability with multiple uncertainty description. To make this clear, consider the block diagrams of Figure 15.16. The block diagram of Figure 15.16a is almost the same as that of Figure 15.13. The block diagram of Figure 15.16b represents a closed-loop feedback system with both multiplicative and inverse multiplicative uncertainties. Referring to Figure 15.16a, in order to satisfy robust performance, condition (15.4-9) must hold. We now focus our attention on Figure 15.16b. To guarantee robust stability, it is necessary and sufficient that the following relation holds j1 þ Gp ð j!Þj > 0; 8Gp ð j!Þ; 8!
Figure 15.16
ð15:4-16Þ
Similarity between robust stability and robust performance: (a) robust performance with multiplicative uncerainty; (b) robust performance with both multiplicative and inverse uncertainty.
Robust Control
659
Block diagram algebra shows that, from condition (15.4-16), we can readily obtain that robust stability is ensured if and only if one of the following two inequalities hold: 1 þ Gð j!Þ½1 þ wm ð j!Þm ð j!Þ½1 wim ð j!Þim ð j!Þ1 > 0; 8m ; im ; 8! ð15:4-17aÞ 1 þ Gð j!Þ þ Gð j!Þwm ð j!Þm ð j!Þ wim ð j!Þim ð j!Þ > 0; 8m ; im ; 8! ð15:4-17bÞ The worst-case (maximum) is obtained in the case where, at each frequency, we select jm ðsÞj ¼ 1, and jim ðsÞj ¼ 1, such that the signs of the terms wim ðsÞim ðsÞ and wm ðsÞGðsÞm ðsÞ are opposite to the sign of 1 þ GðsÞ. In mathematical terms, robust stability is guaranteed if and only if one of the following four equivalent inequalities holds: j1 þ Gð j!Þj jGð j!Þwm ð j!Þj jwim ð j!Þj > 0; 8!
ð15:4-18aÞ
jGð j!Þwm ð j!Þj þ jwim ð j!Þj < j1 þ Gð j!Þj; 8!
ð15:4-18bÞ
jGð j!Þ½1 þ Gð j!Þ1 wm ð j!Þj þ jwim ð j!Þ½1 þ Gð j!Þ1 j < 1; 8!
ð15:4-18cÞ
jwm ð j!ÞTð j!Þj þ jwim ð j!ÞSð j!Þj < 1; 8!
ð15:4-18dÞ
Condition (15.4-18) is equivalent to condition (15.4-9), provided that wim ðsÞ wP ðsÞ. 15.5
KHARITONOV’S THEOREM AND RELATED RESULTS
This section refers mainly to the seminal theorem of Kharitonov [1], which, since its appearance in the late 1970s, has motivated a variety of powerful results (such as the sixteen-plant theorem, the edge theorem [1], etc.) for more general robustness problems. 15.5.1 Kharitonov’s Theorem for Robust Stability Kharitonov’s theorem addresses robust stability of interval polynomials with lumped uncertainty and fixed degree of the form pðs; aÞ ¼
n X
ð15:5-1Þ
ai s i
i¼0
where þ ai 2 ½a i ; ai ;
i ¼ 0; 1; . . . ; n
þ ½a i ; ai
where denotes the a priori known bounding interval for the ith component of uncertainty (the ith uncertain coefficient) ai . To describe Kharitonov’s theorem for robust stability, it is first necessary to define four fixed polynomials associated with a family of interval polynomials. Definition 15.5.1 (Khartinov Polynomials) Associated with the interval polynomial pðs; aÞ are the four fixed Kharitonov polynomials
660
Chapter 15 þ 2 þ 3 4 5 þ 6 p1 ðsÞ ¼ a 0 þ a1 s þ a2 s þ a3 s þ a4 s þ a5 s þ a6 s þ þ 2 3 þ 4 þ 5 6 p2 ðsÞ ¼ aþ 0 þ a1 s þ a2 s þ a3 s þ a4 s þ a5 s þ a6 s þ 2 þ 3 þ 4 5 6 p3 ðsÞ ¼ aþ 0 þ a1 s þ a2 s þ a3 s þ a4 s þ a5 s þ a6 s þ þ þ 2 3 4 þ 5 þ 6 p4 ðsÞ ¼ a 0 þ a1 s þ a2 s þ a3 s þ a4 s þ a5 s þ a6 s þ
The Kharitonov polynomials are easily constructed by inspection, as in the following example. Example 15.5.1 Consider the following interval polynomial with fixed degree 6: pðs; aÞ ¼ ½2; 3s6 þ ½1; 8s5 þ ½3; 12s4 þ ½5; 6s3 þ ½4; 7s2 þ ½9; 11s þ ½6; 15 Derive the four Kharitonov polynomials. Solution For this case, the four Kharitonov polynomials are p1 ðsÞ ¼ 6 þ 9s þ 7s2 þ 6s3 þ 3s4 þ s5 þ 3s6 p2 ðsÞ ¼ 15 þ 11s þ 4s2 þ 5s3 þ 12s4 þ 8s5 þ 2s6 p3 ðsÞ ¼ 15 þ 9s þ 4s2 þ 6s3 þ 12s4 þ s5 þ 2s6 p4 ðsÞ ¼ 6 þ 11s þ 7s2 þ 5s3 þ 3s4 þ 8s5 þ 3s6 We are now in position to present the celebrated Kharitonov’s theorem. Theorem 15.5.1 An interval polynomial pðs; aÞ with invariant degree n is robustly stable if and only if its four associated Kharitonov polynomials are stable. We next give an application example of Kharitonov’s theorem. Example 15.5.2 Consider the following interval polynomial with fixed degree 5: pðs; aÞ ¼ ½1; 3s5 þ ½3; 6s4 þ ½4; 7s3 þ ½5; 9s2 þ ½3; 4s þ ½2; 5 Determine if this interval polynomial is robustly stable. Solution In this case, the four Kharitonov polynomials are p1 ðsÞ ¼ 2 þ 3s þ 9s2 þ 7s3 þ 3s4 þ s5 p2 ðsÞ ¼ 5 þ 4s þ 5s2 þ 4s3 þ 6s4 þ 3s5 p3 ðsÞ ¼ 5 þ 3s þ 5s2 þ 7s3 þ 6s4 þ s5 p4 ðsÞ ¼ 2 þ 4s þ 9s2 þ 4s3 þ 3s4 þ 3s5 According to Kharitonov’s theorem, in order to guarantee robust stability of the given interval polynomial, it is sufficient to test the stability of the above four Kharitonov polynomials. This can be accomplished by using an algebraic stability
Robust Control
661
criterion, e.g., the Routh criterion. Applying the Routh criterion in the four Kharitonov’s polynomials, we obtain the following four Routh tables: 1.
2.
3.
4.
Polynomial p1 ðsÞ: 1 s5 3 s4 4 s3 29=4 s2 s1 107=77 2 s0
7 9 7=3 2 0
Polynomial p2 ðsÞ: 3 4 s5 6 5 s4 3=2 s3 3=2 1 15=2 s2 0 s1 51=4 s0 15=2 Polynomial p3 ðsÞ: 1 s5 4 6 s 3 37=6 s 107=37 s2 s1 909=107 5 s0 Polynomial p4 ðsÞ: 3 s5 3 s4 5 s3 51=5 s2 s1 152=51 2 s0
4 9 2 2 0
3 2 0 0
4 5 0 0
7 5 13=6 5 0
3 5 0 0
4 2 0 0
Since the polynomials p2 ðsÞ, p3 ðsÞ, and p4 ðsÞ are unstable, the interval polynomial pðs; aÞ of the present example is not robustly stable. Kharitonov’s test for robust stability can signficantly be simplified if the interval polynomial studied is of degree 5, 4, or 3. In these cases, the Kharitonov polynomials necessary for performing the test are 3, 2, or 1 in number, respectively, as against the four polynomials of the general case. More precisely, we have the following propositions. Proposition 15.5.1 An interval polynomial pðs; aÞ with invariant degree 5 is robustly stable if and only if the Kharitonov polynomials p1 ðsÞ, p2 ðsÞ, and p3 ðsÞ are stable.
662
Chapter 15
Proposition 15.5.2 An interval polynomial pðs; aÞ with invariant degree 4 is robustly stable if and only if the Kharitonov polynomials p2 ðsÞ and p3 ðsÞ are stable. Proposition 15.5.3 An interval polynomial pðs; aÞ with invariant degree 3 is robustly stable if and only if the Kharitonov polynomial p3 ðsÞ is stable. Although Kharitonov’s theorem is a very important result in the area of robustness analysis, it has several limitations. The most important limitations are the following: (a)
Kharitonov’s theorem is applicable only in problems for which the stability region is the open left-half plane. In other words, Kharitonov’s theorem cannot be applied in the case of discrete-time systems. (b) Kharitonov’s theorem is applicable only in the case of interval polynomials whose coefficients vary independently. In the more general case of interval polynomials of the form pðs; aÞ ¼
n X
fi ða1 ; a2 ; . . . ; am Þsi
ð15:5-2Þ
i¼0
where fi ða1 ; a1 ; . . . ; am Þ; i ¼ 1; 2; . . . ; n are multilinear functions of the uncertain coefficients a1 ; a2 ; . . . ; am , Kharitonov’s theorem fails to give an answer to the question of the robust stability of interval polynomials of the form (15.5-2). Much research effort has been devoted to removing these limitations. An important such effort is the celebrated Edge theorem, which will not be presented here since it is beyond the scope of this book. The interested reader may refer to [1] for a detailed discussion of this important theorem. 15.5.2
The Sixteen-Plant Theorem
Here, we generalize the analytical results presented in the previous subsection, in order to develop a technique for the design of robustly stabilizing compensators. In particular, we focus our attention on the design of proper first-order compensators of the form FðsÞ ¼
Kðs zÞ sp
ð15:5-3Þ
which robustly stabilize a strictly proper interval plant family of the form m X
Aðs; aÞ Gðs; a; bÞ ¼ Bðs; bÞ
aj s j
j¼0
s þ n
n1 X
; bi s
m
ð15:5-4Þ
i
i¼0 þ aj 2 ½a j ; aj ;
j ¼ 0; 1; . . . ; m;
þ bi 2 ½b i ; bi ;
i ¼ 0; 1; . . . ; n 1
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663
þ þ with ½a j ; aj and ½bi ; bi denoting the a priori known bounding intervals for the jth and the ith components of uncertainties aj and bi , respectively. We say that the compensator FðsÞ of the form (15.5-3) robustly stabilizes the þ þ interval plant family (15.5-4), if for all aj 2 ½a j ; aj , j ¼ 0; 1; . . . ; m and bi 2 ½bi ; bi , i ¼ 0; 1; . . . ; n 1, the resulting closed-loop polynomial
pc ðs; a; bÞ ¼ Kðs zÞAðs; aÞ þ ðs pÞBðs; bÞ
ð15:5-5Þ
is Hurwitz (i.e., its roots lie in the open left-half plane). FðsÞ is then called a robust stabilizer of the interval plant family (15.5-4). The focal point of this subsection is the following question. Given the interval plant family (15.5-4), with a compensator interconnected as a Figure 15.17, under what conditions can we establish robust stability of the closed-loop system, under a ‘‘small’’ finite subset of systems corresponding to the extreme members of the family (15.5-4)? We next try to answer this question. To this end, we introduce the Kharitonov polynomials for the numerator and the denominator of Eq. (15.5-4). For the numerator, let þ 2 þ 3 4 5 þ 6 A1 ðsÞ ¼ a 0 þ a1 s þ a2 s þ a3 s þ a4 s þ a5 s þ a6 s þ þ 2 3 þ 4 þ 5 6 A2 ðsÞ ¼ aþ 0 þ a1 s þ a2 s þ a3 s þ a4 s þ a5 s þ a6 s þ 2 þ 3 þ 4 5 6 A3 ðsÞ ¼ aþ 0 þ a1 s þ a2 s þ a3 s þ a4 s þ a5 s þ a6 s þ þ þ 2 3 4 þ 5 þ 6 A4 ðsÞ ¼ a 0 þ a1 s þ a2 s þ a3 s þ a4 s þ a5 s þ a6 s þ
and for the denominbtor let þ 2 þ 3 4 5 þ 6 B1 ðsÞ ¼ b 0 þ b 1 s þ b2 s þ b3 s þ b4 s þ b5 s þ b6 s þ þ 2 3 þ 4 þ 5 6 B2 ðsÞ ¼ bþ 0 þ b 1 s þ b2 s þ b3 s þ b4 s þ b5 s þ b6 s þ 2 þ 3 þ 4 5 6 B3 ðsÞ ¼ bþ 0 þ b 1 s þ b2 s þ b3 s þ b4 s þ b5 s þ b6 s þ þ þ 2 3 4 þ 5 þ 6 B4 ðsÞ ¼ b 0 þ b 1 s þ b2 s þ b3 s þ b4 s þ b5 s þ b6 s þ
By taking all combinations of the Ai ðsÞ, i ¼ 1, 2, 3, 4, and Bk ðsÞ, k ¼ 1, 2, 3, 4, we obtain the 16 Kharitonov plants Gik ðsÞ ¼
Ai ðsÞ Bk ðsÞ
ð15:5-6Þ
for i; k ¼ 1, 2, 3, 4. For these extreme plants, when it is said that FðsÞ stabilizes Gik ðsÞ, we understand that the closed-loop polynomial pc;ik ðsÞ ¼ Kðs zÞAi ðsÞ þ ðs pÞBk ðsÞ
Figure 15.17
Interval plant family interconnected with a first-order compensator.
ð15:5-7Þ
664
Chapter 15
is asymptotically stable. We are now able to state the so-called sixteen-plant theorem. Theorem 15.5.2 A proper first-order compensator of the form (15.5-3) robustly stabilizes the interval plant family (15.5-4), if and only if it stabilizes all of the 16 Kharitonov plants Gik ðsÞ, i; k ¼ 1, 2, 3, 4. We next give some examples regarding the 16 Kharitonov plants and the application of the sixteen-plant theorem as a design tool. Example 15.5.3 Consider the interval plant family Gðs; a; bÞ ¼
½2; 5s2 þ ½6; 9s þ ½3; 11 s3 þ ½4; 6s2 þ ½1; 8s þ ½5; 7
Determine one proper first-order compensator that can robustly stabilize the family and one that cannot. Solution The 16 Kharitonov plants associated with this family are 2s2 þ 6s þ 11 ; þ 4s2 þ s þ 7
G12 ðsÞ ¼
G13 ðsÞ ¼
2s2 þ 6s þ 11 ; s3 þ 6s2 þ s þ 5
G14 ðsÞ ¼
2s2 þ 6s þ 11 s3 þ 4s2 þ 8s þ 7
G21 ðsÞ ¼
5s2 þ 9s þ 3 ; s3 þ 4s2 þ s þ 7
G22 ðsÞ ¼
5s2 þ 9s þ 3 s3 þ 6s2 þ 8s þ 5
G23 ðsÞ ¼
5s2 þ 9s þ 3 ; s3 þ 6s2 þ s þ 5
G24 ðsÞ ¼
5s2 þ 9s þ 3 s3 þ 4s2 þ 8s þ 7
G31 ðsÞ ¼
5s2 þ 6s þ 3 ; s3 þ 4s2 þ s þ 7
G32 ðsÞ ¼
5s2 þ 6s þ 3 s3 þ 6s2 þ 8s þ 5
G33 ðsÞ ¼
5s2 þ 6s þ 3 ; s þ 6s2 þ s þ 5
G34 ðsÞ ¼
5s2 þ 6s þ 3 s þ 4s2 þ 8s þ 7
G41 ðsÞ ¼
2s2 þ 9s þ 11 ; s3 þ 4s2 þ s þ 7
G42 ðsÞ ¼
2s2 þ 9s þ 11 s3 þ 6s2 þ 8s þ 5
G43 ðsÞ ¼
2s2 þ 9s þ 11 ; s3 þ 6s2 þ s þ 5
G44 ðsÞ ¼
2s2 þ 9s þ 11 s3 þ 4s2 þ 8s þ 7
G11 ðsÞ ¼
s3
3
s3
2s2 þ 6s þ 11 þ 6s2 þ 8s þ 5
3
With a particular first-order compensator, say FðsÞ ¼ ðs 1Þ=ðs þ 1Þ, it can easily be verified that the closed-loop polynomial, associated with the Kharitonov plant G32 ðsÞ, is given by pc;32 ðsÞ ¼ ðs 1Þð5s2 þ 6s þ 3Þ þ ðs þ 1Þðs3 þ 6s2 þ 8s þ 5Þ ¼ s4 þ 12s3 þ 15s2 þ 10s þ 2
Robust Control
665
It is not difficult to show that the controller f ðsÞ ¼ ðs 1Þ=ðs þ 1Þ cannot robustly stabilize the given interval plant family. Indeed, with this controller, the closed-loop polynomial pc;11 ðsÞ is given by pc;11 ðsÞ ¼ ðs 1Þð2s2 þ 6s þ 11Þ þ ðs þ 1Þðs3 þ 4s2 þ s þ 7Þ ¼ s4 þ 7s3 þ 9s2 þ 13s 4 Since there is a sign change in the coefficients of pc;11 ðsÞ, the polynomial is not stable. Therefore, FðsÞ ¼ ðs 1Þ=ðs þ 1Þ cannot robustly stabilize the entire interval plant family. On the other hand, as can easily be checked by performing 16 Routh tests for closed-loop polynomials of the 16 Kharitonov plants, the PI controller of the form FðsÞ ¼ 1 þ
1 s
robustly stabilizes the given interval plant family. Example 15.5.4 Although the sixteen-plant theorem is stated as an analysis tool, it can be quite easily used in a synthesis context as well. Explain how this is possible. Solution Consider the interval plant family Gðs; a; bÞ ¼
½1; 1:5s þ ½0:5; 1 s þ ½2; 3s2 þ ½1; 2s þ ½3; 4 3
Suppose that one wants to find a robustly stabilizing PI controller of the form FðsÞ ¼ K1 þ K2 =s
ð15:5-8Þ
for the above family. Note that a PI controller of the form (15.5-8) is the special case of a first-order compensator of the form (15.5-3), for which p ¼ 0, K ¼ K1 , and z ¼ K2 =K1 . To deal with this problem, we first construct the 16 Kharitonov plants associated with the given interval plant family. These are the following: s þ 0:5 ; s þ 2s2 þ s þ 4 s þ 0:5 G13 ðsÞ ¼ 3 ; s þ 3s2 þ s þ 3 1:5s þ 1 G21 ðsÞ ¼ 3 ; s þ 2s2 þ s þ 4 1:5s þ 1 G23 ðsÞ ¼ 3 ; s þ 3s2 þ s þ 3 1:5s þ 0:5 G31 ðsÞ ¼ 3 ; s þ 2s2 þ s þ 4 1:5s þ 0:5 G33 ðsÞ ¼ 3 ; s þ 3s2 þ s þ 3
G11 ðsÞ ¼
3
s þ 0:5 s þ 3s2 þ 2s þ 3 s þ 0:5 G14 ðsÞ ¼ 3 s þ 2s2 þ 2s þ 4 1:5s þ 1 G22 ðsÞ ¼ 3 s þ 3s2 þ 2s þ 3 1:5s þ 1 G24 ðsÞ ¼ 3 s þ 2s2 þ 2s þ 4 1:5s þ 0:5 G32 ðsÞ ¼ 3 s þ 3s2 þ 2s þ 3 1:5s þ 0:5 G34 ðsÞ ¼ 3 s þ 2s2 þ 2s þ 4 G12 ðsÞ ¼
3
666
Chapter 15
sþ1 ; s3 þ 2s2 þ s þ 4 sþ1 G43 ðsÞ ¼ 3 ; s þ 3s2 þ s þ 3
sþ1 s3 þ 3s2 þ 2s þ 3 sþ1 G44 ðsÞ ¼ 3 s þ 2s2 þ 2s þ 4
G41 ðsÞ ¼
G42 ðsÞ ¼
With the PI compensator of the form (15.5-8), the closed-loop polynomials associated with the 16 Kharitonov plants can be easily obtained. For example, for the Kharitonov plant G42 ðsÞ, the associated closed-loop polynomials, is found to be pc;42 ðsÞ ¼ sðs3 þ 3s2 þ 2s þ 3Þ þ ðK1 s þ K2 Þðs þ 1Þ ¼ s4 þ 3s3 þ ð2 þ K1 Þs2 þ ð3 þ K1 þ K2 Þs þ K2 Now, using the polynomial pc;42 ðsÞ, we generate the Routh table 1 2 þ K1 K2 s4 3 3 3 þ K þ K 0 s 1 2 2 ðK ; K Þ K a s 1 1 2 2 0 s1 a2 ðK1 ; K2 Þ K2 s0 where a1 ðK1 ; K2 Þ ¼
3 þ 2K1 K2 3
and
a2 ðK1 ; K2 Þ ¼ 3 þ K1 þ K2
3K2 a1 ðK1 ; K2 Þ
In a similar manner one can generate Routh tables for the remaining 15 Kharitonov plants. Using the 16 Routh tables our next steps is to enforce positivity in each of the first columns. As a result, we obtain inequalities involving K1 and K2 . For example, for the Routh table corresponding to the Kharitonov polynomial pc;42 ðsÞ, positivity of the first column leads to the conditions K1 >
K2 3 2
and
2K12 K22 þ K1 K2 þ 9K1 3K2 þ 9 > 0
ð15:5-9Þ
It is not difficult to plot the set of the gains K1 and K2 , satisfying condition (15.5-9). This set is shown in Figure 15.18, for the range 0 < K1 < 70, 0 < K2 < 70. The set of stabilizing PI controllers for each of the remaining 15 Kharitonov plants can be obtained in a similar way. Then, the desired set of robustly stabilizing controllers for the interval plant family is obtained as the cross-section of the above 16 particular stabilizing sets. In our case, the set K of robust PI stabilizers of the form (15.5-8) is shown in Figure 15.19. This set is obviously nonempty, and the given interval plant family is robustly stabilizable. To stabilize the interval plant family, we can choose any pair ðK1 ; K2 Þ which belongs to the set K. For example, a robust PI stabilizer is given by FðsÞ ¼ 20 þ
10 s
Before closing this section, we point out that the sixteen-plant theorem can be extended to the more general class of compensators FðsÞ ¼
Kðs zÞ ; sq ðs pÞ
q>1
Robust Control
Figure 15.18
667
The set of stabilizing PI controllers for G42 ðsÞ.
Unfortunately, the sixteen-plant theorem does not hold in the case of more general classes of compensators, or in the case of more general classes of interval plant families. In these cases, one may use other important results (such as the ‘‘sixtyfour polynomial approach’’ or the ‘‘4k polynomial approach’’) to characterize the stability of an interval plant family, by performing tests in a finite number of characteristic plants (see [4] for details). It should, however, be noted that in these situations, the issue of the computational effort needed to find such a compensator
Figure 15.19
The set of robust PI stabilizers for the interval plant family Gðs; a; bÞ.
668
Chapter 15
is of paramount importance, since the number of parameters entering FðsÞ may be very large. PROBLEMS 1. Consider the set of plants with parametric uncertainty given by relation (15.2-9). Show that the additive form of the uncertainty set (15.2-9) is given by Gp ðsÞ ¼ GðsÞ þ wa ðsÞa ;
ja j 1
where GðsÞ ¼
G0 ðsÞ ; 1 þ m s
wa ðsÞ ¼
r m s ð1 þ m sÞ2
and where min þ max ; 2
m ¼
r ¼
max min max þ min
2. Consider the set of plants with parameters uncertainty given by P : Gp ðsÞ ¼
3ðs þ 1Þ ; ðas þ 1Þðbs þ 1Þ
amin a amax ;
bmin b bmax
Show that the above set of plants can be set in the following inverse multiplicative uncertainty form P : Gp ðsÞ ¼ GðsÞ½1 þ wim ðsÞ1 ;
jj 1
where GðsÞ ¼
sþ1 ðam s þ 1Þðbm s þ 1Þ þ ra rb am bm s2
wim ðsÞ ¼
½ra am ðbm s þ 1Þ þ rb bm ðam s þ 1Þs ðam s þ 1Þðbm s þ 1Þ þ ra rb am bm s2
and where am ¼ ra ¼
amin þ amax ; 2
amax amin ; amax þ amin
bm ¼ rb ¼
bmin þ bmax 2
bmax bmin bmax þ bmin
3. Consider the family of plants with parametric uncertainty given by P : Gp ðsÞ ¼
3ðas þ 1Þ ; ð2s þ 1Þðbs þ 1Þ2
1 a 2;
2b3
Suppose that we want to obtain a multiplicative uncertainty description of the above family. Plot the smallest radius ‘m ð!Þ and approximate it by a rational transfer function weight wm ðsÞ. Show that two good choices for the multiplicative uncertainty weight are
Robust Control
669
wm ðsÞ ¼
5s þ 1 2s þ 3
wm ðsÞ ¼
and
s2 þ 3s þ 0:01 0:7s2 þ 3s þ 1
Also, show that the approximation of ‘m ð!Þ by wm ðsÞ ¼
5s þ 1 2s þ 7
or
wm ðsÞ ¼
s2 2s þ 3s þ 1 2
is not good enough to represent in the multiplicative uncertainty form the given family of plants. 4. Consider the uncertain feedback control system of Figure 15.5. Assume that the uncertain plant transfer function is given by Hp ðsÞ ¼ HðsÞ½1 þ wm ðsÞm ðsÞ where HðsÞ ¼
sþ2 s 2s þ 1 2
and
wm ðsÞ ¼
3s þ 1 2s þ 10
while the controller KðsÞ is a PI controller of the form K1 ðsÞ ¼ 5 þ
10 s
Show that the closed-loop systems is robustly stable. Repeat the test with the PI controller of the form K2 ðsÞ ¼ 5
10 s
and verify that, in this case, the closed-loop system is not robustly stable. 5. Consider the feedback system of Figure 15.8. Assume that the uncertain plant transfer function is given by Hp ðsÞ ¼ HðsÞ½1 þ wim ðsÞim ðsÞ1 where HðsÞ ¼
sþ2 s2 2s þ 1
and
wim ðsÞ ¼
sþ1 2s þ 5
while the controller KðsÞ is a PI controller of the form 5 KðsÞ ¼ 3 þ s Show that the closed-loop system is robustly stable. 6. Consider the feedback system of Figure 15.10, with sþ3 3 HðsÞ ¼ 2 and KðsÞ ¼ 2 þ s s 2s þ 3 The design specifications for the closed-loop system are (a) Steady-state tracking error A ¼ 0 (b) Maximum peak magnitude M ¼ 2 of the sensitivity function SðsÞ (c) Minimum bandwidth ! B ¼ 0:2 rad/sec Show that the closed-loop system satisfies the above performance specifications.
670
Chapter 15
7. Consider the feedback control system of Figure 15.13, with sþ3 ; s2 2s þ 3 s þ 0:4 wP ðsÞ ¼ s HðsÞ ¼
4 KðsÞ ¼ 3 ; s
wm ðsÞ ¼
3s þ 1 ; 2s þ 10
Show that the closed-loop system does not satisfy the robust performance specification. Show that the same is true if the controller has the form KðsÞ ¼ 7 (i.e., the form of a constant gain controller). Repeat the test with the controller KðsÞ ¼ 1 þ 5=s, and verify that in this case the closed-loop system meets the robust performance specification. 8. Consider the interval polynomial family with fixed degree pðs; aÞ ¼ ½3; 4:5s6 þ ½5; 8s5 þ ½6; 8s4 þ ½7; 11s3 þ ½4; 5s2 þ ½1; 5s þ ½2; 13 Construct the four Kharitonov polynomials related to this family. Is this polynomial family robustly stable? Repeat the same test with the interval polynomial family pðs; aÞ ¼ ½2; 7s5 þ ½8; 10s4 þ ½4; 7s3 þ ½4; 5s2 þ ½3; 5s þ ½9; 11 9. Consider the interval plant family Gðs; a; bÞ ¼
½2; 3s þ ½1; 3 s2 þ ½2; 4s þ ½1; 2:5
Show that the first-order compensator of the form FðsÞ ¼
s1 s2
cannot robustly stabilize the given interval plant family. Show that the entire family can be robustly stabilized by the use of a simple gain controller of the form FðsÞ ¼ K;
K >0
10. Consider the interval plant family Gðs; a; bÞ ¼
s2
½1; 2s þ ½2; 3 þ ½2; 5s þ ½2; 6
Let a first-order compensator of the form FðsÞ ¼
s þ K1 s þ K2
be connected to the given plant family, as suggested in Figure 15.17. Find the ranges of K1 and K2 for which the closed-loop system is robustly stable. Show, by using any two-variable graphic (e.g., the version provided by Matlab), that the range of K1 and K2 has the form shown in Figure 15.20.
Robust Control
Figure 15.20
671
The set of robust stabilizers for Problem 10.
REFERENCES Books 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
BR Barmish. New Tools for Robustness of Linear Systems. New York: MacMillan, 1994. HW Bode. Network Analysis and Feedback Amplifier Design. Princeton, New Jersey: Van Nostrand, 1945. FM Callier, CA Desoer. Multivariable Feedback Systems. New York: Springer-Verlag, 1982. TE Djaferis. Robust Control Design: A Polynomial Approach. Boston, MA: Kluwer Academic Publishers, 1995. BA Francis. A Course in H1 Control. Berlin: Springer-Verlag, 1987. AGJ McFarlane (ed). Frequency Response Methods in Control Systems. New York: IEEE Press, 1979. HH Rosenbrock. State Space and Multivariable Theory. London: Nelson, 1970. HH Rosenbrock. Computer-Aided Control System Design. New York: Academic Press, 1974. M Vidyasagar. Control System Synthesis—A Factorization Approach. Cambridge, MA: MIT Press, 1985. N Wiener. Extrapolation, Interpolation and Smoothing of Stationary Time Series with Engineering Applications. New York: Wiley, 1949.
Articles 11.
MA Athans. The role and the use of the stochastic linear-quadratic-gaussian problem. IEEE Trans Automatic Control AC-16:529–552, 1971. 12. BC Chang, JB Pearson. Optimal disturbance rejection in linear multivariable systems. IEEE Trans Automatic Control AC-29:880–887, 1984. 13. PH Delsarte, Y Genin, Y Kamp. The Nevanlinna–Pick problem in circuit and system theory. Int J Circuit Th Appl 9:177–187, 1981. 14. CA Desoer, RW Liu, J Murrary, R Saeks. Feedback system design: the fractional representation approach to analysis and synthesis. IEEE Trans Automatic Control AC25:339–412, 1980.
672 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29.
30. 31.
32.
Chapter 15 JC Doyle, K Glover, PP Khargonekar, BA Francis. State-space solutions to standard H2 and H1 control problems. IEEE Trans Automatic Control AC-34:831–847, 1989. JC Doyle, G Stein. Multivariable feedback design: concepts for a classical modern synthesis. IEEE Trans Automatic Control AC-26:4–16, 1981. BA Francis, JW Helton, G Zames. H1 optimal controllers for linear multivariable systems. IEEE Trans Automatic Control AC-29:888–920, 1984. BA Francis, G Zames. On H1 optimal sensitivity theory for SISO systems. IEEE Trans Automatic Control AC-29:9–16, 1984. K Glover. All optimal Hankel norm approximation of linear multivariable systems and their L1 error bounds. Int J Control 39:1115–1193, 1984. JW Helton. Worst case analysis in the frequency domain: the H1 approach to control. IEEE Trans Automatic Control AC-30:1154–1170, 1985. RE Kalman. Contribution to the theory of optimal control. Bol Socied Mathematica Mexicana, pp 102–119, 1960. RE Kalman. Mathematical description of linear dynamical systems. SIAM J Control 1:151–192, 1963. RE Kalman. When is a linear system optimal. ASME Trans Series D, J Basic Eng 86:51– 60, 1964. RE Kalman. Irreducible realizations and the degree of rational matrices. SIAM J Appl Math 13:520–544, 1965. H Kimura. Robust stabilization of a class of transfer functions. IEEE Trans Automatic Control AC-29:778–793, 1984. AN Kolmogorov. Interpolation and extrapolation of stationary random sequences. Bull Acad Sci USSR Vol 5, 1941. N Nyquist. Regenerative theory. Bell Syst Tech J January issue, 1932. DC Youla, JJ Bongiorno, CN Lu. Modern Wiener–Hopf design of optimal controllers. Part I: The single input case. IEEE Trans Automatic Control AC-21:3–14, 1976. DC Youla, HA Jabar, JJ Bongiorno. Modern Wiener–Hopf design of optimal controllers, Part II. The multivariable case. IEEE Trans Automatic Control AC-21:319–338, 1976. M Vidyasagar, H Kimura. Robust controllers for uncertain linear multivariable systems. Automatica 22:85–94, 1986. G Zames. Feedback and optimal sensitivity: model reference transformations, multiplicative seminorms and approximate inverses. IEEE Trans Automatic Control AC-26:301– 320, 1981. G Zames, BA Francis. Feedback, minimax sensitivity and optimal robustness. IEEE Trans Automatic Control AC-28:585–601, 1983.
16 Fuzzy Control
16.1
INTRODUCTION TO INTELLIGENT CONTROL
In the last two decades, a new approach to control has gained considerable attention. This new approach is called intelligent control (to distinguish it from conventional or traditional control) [1]. The term conventional control refers to theories and methods that are employed to control dynamic systems whose behavior is primarily described by differential and difference equations. Thus, all the well-known classical and statespace techniques in this book fall into this category. The term ‘‘intelligent control’’ has a more general meaning and addresses more general control problems. That is, it may refer to systems which cannot be adequately described by a differential/difference equations framework but require other mathematical models, as for example, discrete event system models. More often, it treats control problems, where a qualitative model is available and the control strategy is formulated and executed on the basis of a set of linguistic rules [2, 5, 10, 12, 14, 16, 33–36]. Overall, intelligent control techniques can be applied to ordinary systems and more important to systems whose complexity defies conventional control methods. There are three basic approaches to intelligent control: knowledge-based expert systems, fuzzy logic, and neural networks. All three approaches are interesting and very promising areas of research and development. In this book, we present only the fuzzy logic approach. For the interested reader, we suggest references [6] and [9] for knowledge-based systems and neural networks. The fuzzy control approach has been studied intensively in the last two decades and many important theoretical, as well as practical, results have been reported. The fuzzy controller is based on fuzzy logic. Fuzzy logic was first introduced by Zadeh in 1965 [33], whereas the first fuzzy logic controller was implemented by Mamdani in 1974 [17]. Today, fuzzy control applications cover a variety of practical systems, such as the control of cement kilns [3, 19], train operation [32], parking control of a car [27], heat exchanger [4], robots [23], and are in many other systems, such as home appliances, video cameras, elevators, aerospace, etc. In this chapter, a brief introduction to fuzzy control is presented (see also Chap. 11 in [11]). This material aims to give the reader the heuristics of this approach 673
674
Chapter 16
to control, which may be quite useful in many practical control problems, but treats the theoretical aspects in an introductory manner only. Furthermore, we hope that this material will inspire further investigation, not only in the area of fuzzy control but also in the more general area of intelligent control. For further reading on the subject of fuzzy control, see the books [1–16] and articles [17–36] cited in the Bibliography. 16.2
GENERAL REMARKS ON FUZZY CONTROL
A principal characteristic of fuzzy control is that it works with linguistic rules (such as ‘‘if the temperature is high then increase cooling’’) rather than with mathematical models and functional relationships. With conventional control, the decisions made by a controller are a rigid ‘‘true’’ or ‘‘false.’’ Fuzzy control uses fuzzy logic, which is much closer in spirit to human thinking and natural language than conventional control systems. Furthermore, fuzzy logic facilitates the computer implementation of imprecise (fuzzy) statements. Fuzzy logic provides an effective means of capturing the approximate and inexact nature of the real world. To put it simply, the basic idea in fuzzy logic, instead of specifying a truth or falsehood, 0 or 1, etc., is to exert a gradual transition depending on the circumstances. For example, an air conditioning unit using conventional control recognizes room temperature only as warm, when the temperature is greater than 218C, and cold, when the temperature is less than 218C. Using fuzzy control, room temperature can be recognized as cold, cool, comfortable, warm, or hot and, furthermore, if this temperature is increasing or decreasing. On the basis of these fuzzy variables, a fuzzy controller makes its decision on how to cool the room. In Figure 16.1a–c the fuzzy notion of cold, hot, and comfortable are presented in graphical form. The magnitude of these graphical representations lies between 0 and 1. The whole domain of fuzzy variables referring to the notion of temperature may be constructed by adding other variables such as cool, warm, etc., as shown in Figure 16.1d. 16.3
FUZZY SETS
A nonfuzzy set (or class) is any collection of items (or elements or members) which can be treated as a whole. Consider the following examples: 1.
2. 3.
The set of all positive integers less than 11. This is a finite set of 10 members, i.e., the numbers 1; 2; 3; . . . ; 9; 10. This set is written as f1; 2; . . . ; 9; 10g. The set of all positive integers greater than 4. This set has an infinite number of members and can be written as x > 4. The set of all humans having four eyes. This set does not have any members and is called an empty (or null) set.
In contrast to nonfuzzy (or crisp) sets, in a fuzzy set there is no precise criterion for membership. Consider for example the set middle-aged people. What are the members of this set? Of course, babies or 100-year-old people are not middle-aged people! One may argue that people from 40 to 60 appear to be in the set of middleaged people! This may not, however, hold true for all people, in all places and at all
Fuzzy Control
Figure 16.1
675
The fuzzy notion of the variable temperature.
times. For example, centuries ago, in most countries, the mean life expectancy was around 50 (this is true today for certain underdeveloped countries). We may now ask: are the ages 32, 36, 38, 55, 58, 60, and 65 members of the set of middle-aged people? The answer is that the set ‘‘middle-aged people’’ is a fuzzy set, where there is no precise criterion for membership and depends on time, place, circumstances, on the subjective point of view, etc. Other examples of fuzzy sets are intelligent people, tall people, strong feelings, strong winds, bad weather, feeling ill, etc. To distinguish between members of a fuzzy set which are more probable than those which are less probable in belonging to the set, we use the grade of membership,
676
Chapter 16
denoted by , which lies in the range of 0 to 1, meaning that as gets closer to 1, the grade of membership becomes higher. If ¼ 1, then it is certainly a member and, of course, if ¼ 0, then it is certainly not a member. The elements of a fuzzy set are taken from a universe. The universe contains all elements. Consider the examples: (a)
The set of numbers from 1 to 1000. The elements are taken from the universe of all numbers. (b) The set of tall people. The elements are taken from the universe of all people. If x is an element of a fuzzy set, then the associated grade of x with its fuzzy set is described via a membership function, denoted by ðxÞ. There are two methods for defining fuzzy sets, depending on whether the universe of discourse is discrete or continuous. In the discrete case, the grade of membership function of a fuzzy set is represented as a vector whose dimension depends on the degree of discretization. An example of a discrete membership function, referring to the fuzzy set middle ¼ ð0:6=30; 0:8=40; 1=50; 0:8=60; 0:6=70Þ, where the universe of discourse represents the ages ½0; 100. This is a bell-shaped membership function. In the continuous case, a functional definition expresses the membership function of a fuzzy set in a functional form. Some typical examples of continuous membership functions are given below: "
ðx x0 Þ2
ðxÞ ¼ exp 2 2
ðxÞ ¼ 1 þ
# ð16:3-1Þ
x x 2 1 0
ð16:3-2Þ
ðxÞ ¼ 1 exp
x0 x
ð16:3-3Þ
where x0 is the point where ðxÞ is maximum (i.e., ðx0 Þ ¼ 1Þ and is the standard deviation. Expression (16.3-1) is the well-known standard Gaussian curve. In expression (16.3-3) the exponent shapes the gradient of the sloping sides. To facilitate our understanding further, we refer to Figure 16.2, where the very simple case of the fuzzy and nonfuzzy interpretation of an old man is given. In the nonfuzzy or crisp case, everyone older than 70 is old, whereas in the fuzzy case the transition is gradual. This graphical presentation reveals the distinct difference between fuzzy and nonfuzzy (or crisp) sets. Next, consider the membership functions given in Figure 16.3. Figure 16.3a presents the membership function ðxÞ, defined as follows: 1; 6 x 10 ð16:3-4Þ
ðxÞ ¼ 0; otherwise The nonfuzzy (or crisp) membership function is unique. The corresponding fuzzy membership function may have several forms: triangular (16.3b), bell-shaped curve (16.3c), trapezoidal or flattened bell-shaped (16.3d), etc.
Fuzzy Control
677
Figure 16.2 The fuzzy and nonfuzzy interpretation of an old man. In the nonfuzzy case, anyone older than 70 is old; in the fuzzy case, the transition is gradual.
In fuzzy sets, the variable x may be algebraic, as in relations (16.3-1)–(16.3-3), or it may be a linguistic variable. A linguistic variable takes on words or sentences as values. This type of a value is called a term set. For example, let the variable x be the linguistic variable ‘‘age.’’ Then, one may construct the following term: {very young, young, middle age, old, very old}. Note that each term in the set (e.g., young) is a fuzzy variable itself. Figure 16.4 shows three sets: young (Y), middle age (M), and old (O). Figure 16.5 shows four sets: young (Y), very young (VY), old (O), and very old (VO). We say that the sets ‘‘young’’ and ‘‘old’’ are primary sets, whereas the sets ‘‘very young’’ and ‘‘very old’’ are derived from them.
Figure 16.3 Graphical representation of the membership function (10.3-4): (a) crisp membership funciton; (b) triangular membership function; (c) bell-shaped membership function; (d) trapezoidal membership function.
678
Chapter 16
Figure 16.4
The membership curves of the three sets: young (Y), middle age (M), and old
(O).
16.4
FUZZY CONTROLLERS
The nonfuzzy (crisp) PID controller has been presented in Chap. 9 (continuous-time) and in Chap. 12 (discrete-time), where it was pointed out that this type of controller has many practical merits and has become the most popular type of controller in industrial applications. The same arguments hold true for the fuzzy PID controller and, for this reason, we focus our attention on this controller. We will examine, in increasing order of complexity, the fuzzy proportional (FP), the fuzzy proportional– derivative (FPD), and the fuzzy proportional–derivative plus integral (FDP+I) controller. 1 The FP Controller Let eðkÞ be the input and uðkÞ be the output of the controller, respectively. The input eðkÞ is the error eðkÞ ¼ rðkÞ yðkÞ
ð16:4-1Þ
where rðkÞ is the reference signal and yðkÞ is the output of the system (see any closedloop figure in the book, or Figure 16.9 in Sec. 16.5 that follows). Then
Figure 16.5
The membership curves of the four sets: very young (VY), young (Y), old (O), and very old (VO).
Fuzzy Control
Figure 16.6
679
The fuzzy proportional (FP) controller.
uðkÞ ¼ f ðeðkÞÞ
ð16:4-2Þ
that is, the output of the controller is a nonlinear function of eðkÞ. A simplified diagram of Eq. (16.4-2) is given in Figure 16.6. In comparison with Figure 12.32 of Chap. 12, where only one tuning parameter appears (the parameter Kp), for FP controllers we have two tuning parameters, namely the parameters Ge and Gu , which are the error and controller output gains, respectively. The block designated as ‘‘rule base’’ is the heart of the fuzzy controller whose function is explained in Secs 16.5 and 16.6 below. 2 The FPD Controller Let ceðkÞ denote the change in the error (for continuous-time systems, ceðkÞ corresponds to the derivative of the error de=dt). An approximation to ceðtÞ is given by ceðkÞ ¼
eðkÞ eðk 1Þ T
ð16:4-3Þ
where T is the sampling time. The block diagram of the FPD controller is given in Figure 16.7. Here, the output of the controller is a nonlinear function of two variables, namely the variables eðkÞ and ceðkÞ, i.e., uðkÞ ¼ f ðeðkÞ; ceðkÞÞ
ð16:4-4Þ
Note that here we have three tuning gains (Ge ; Gce , and Gu ), as compared with the crisp PI controller, which has only two (see Sec. 12.10). 3 The PFD+I Controller It has been shown that it is not straightforward to write rules regarding integral action. Furthermore, the rule base involving three control actions (proportional, derivative, and integral) simultaneously becomes very large. To circumvent these difficulties, we separate the integral action from the other two actions, resulting in an FPD+I controller, as shown in Figure 16.8. In the present case the output of the
Figure 16.7
The fuzzy proportional–derivative (FPD) controller.
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Figure 16.8
The fuzzy proportional–derivative and integral (FPD+I) controller.
controller uðkÞ is a nonlinear function of three variables, namely the variables eðkÞ, ceðkÞ, and ieðkÞ, i.e., uðkÞ ¼ f ðeðkÞ; ceðkÞ; ieðkÞÞ
ð16:4-5Þ
where ieðkÞ denotes the integral of the error. Since the integral action has been separated from the proprotional and derivative actions, relation (16.4-5) breaks down to two terms: uðkÞ ¼ u1 ðkÞ þ u2 ðkÞ ¼ f1 ðeðkÞ; ceðkÞÞ þ f2 ðieðkÞÞ
ð16:4-6Þ
Note that here we have four tuning parameters (Ge ; Gce ; Gie , and Gu ) as compared with the crisp PID controller of Sec. 12.10, which has only three. 16.5
ELEMENTS OF A FUZZY CONTROLLER
A simplified block diagram of a fuzzy controller incorporated in a closed-loop system is shown in Figure 16.9. The fuzzy controller involves four basic operations, namely the fuzzification interface, the rule base, the inference engine, and the defuz-
Figure 16.9
Basic configuration of a closed-loop system involving an FLC, wherein the fuzzy and crisp data flow is identified.
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zification interface. A brief explanation of these four elements is given below. A more detailed explanation is given in the next four sections. 1 Fuzzification Interface Here, the crisp error signal eðkÞ is converted into a suitable linguistic fuzzy set. 2 Rule Base The rule base is the heart of a fuzzy controller, since the control strategy used to control the closed-loop system is stored as a collection of control rules. For example, consider a controller with three inputs e1 , e2 , and e3 and output u. Then, a typical control rule has the form if e1 is A; e2 is B; and e3 is C; then u is D
ð16:5-1Þ
where A, B, C, and D are linguistic terms, such as very low, very high, medium, etc. The control rule (16.5-1) is composed of two parts: the ‘‘if’’ part and the ‘‘then’’ part. The ‘‘if’’ part is the input to the controller and the ‘‘then’’ part is the output of the controller. The ‘‘if’’ part is called the premise (or antecedent or conditon) and the ‘‘then’’ part is called the consequence (or action). 3 Inference Engine The basic operation of the interference engine is that it ‘‘infers,’’ i.e., it deduces (from evidence or data) a logical conclusion. Consider the following example described by the logical rule, known as modus ponens: Premise 1: If an animal is a cat, then it has four legs. Premise 2: My pet is a cat. _______________________________________________________ Conclusion: My pet has four legs. Here, premise 1 is the base rule, premise 2 is the fact (or the evidence or the data), and the conclusion is the consequence. The inference engine is a program that uses the rule base and the input data of the controller to draw the conclusion, very much in the manner shown by the above modus ponens rule. The conclusion of the inference engine is the fuzzy output of the controller, which subsequently becomes the input to the defuzzification interface. 4 Defuzzification Interface In this last operation, the fuzzy conclusion of the inference engine is defuzzified, i.e., it is converted into a crisp signal. This last signal is the final product of the fuzzy logic controller (FLC), which is, of course, the crisp control signal to the process. The above four operations are explained in greater detail in the sections that follow. 16.6
FUZZIFICATION
The fuzzification procedure consists of finding appropriate membership functions to describe crisp data. For example, let speed be a linguistic variable. Then the set T(speed) could be
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Figure 16.10 The membership function for the term set T(speed) ¼ fslow, medium, fastg. (a) Slow speed (shouldered); (b) medium speed (triangular); (c) high speed (shouldered). TðspeedÞ ¼ fslow, medium, fastg
ð16:6-1Þ
On a scale from 0 to 100, slow speed may be up to 35, medium speed could be from 30 to 70, and high speed could be from 65 to 100. The membership functions for the three fuzzy variables may have several shapes. Figure 16.10 shows some membership functions for each of the three fuzzy variables. Other examples of fuzzification of crisp data have already been presented in Figures 16.1, 16.2, 16.3b–d, 16.4 and 16.5. 16.7
THE RULE BASE
The most usual source for constructing linguistic control rules are human experts. We start by questioning experts or operators using a carefully prepared questionaire. Using their answers, a collection of if–then rules is established. These rules contain all the information regarding the control of the process. Note that there are other types of sources for constructing the rule base, such as control engineering knowledge, fuzzy models, etc. [20]. The linguistic control rules are usually presented to the end-user in different formats. One such format has the verbal form of Table 16.1 which refers to the twoinput one-output controller of Figure 16.7 for the control of the temperature of a room. Here, the controller inputs e and ce refer to the error and change in error, respectively, whereas the variable u refers to the output of the controller. This format involves the following five fuzzy sets: zero (Z), small positive (SP), large positive (LP), small negative (SN), and large negative (LN). Clearly, the set of if–then rules presented in Table 16.1 is an example of a linguistic control strategy applied by the controller in order to maintain the room temperature close to the desired optimum value of 218C. In Figure 16.11 the graphical representation of the five fuzzy sets Z, SP, LP, SN, and LN is given. Using Figure 16.11, the graphical forms of the nine rules of Table 16.1 are presented in Figures 16.12 and 16.13. 16.8
THE INFERENCE ENGINE
The task of the inference engine is to deduce a logical conclusion, using the rule base. To illustrate how this is performed, we present three examples, in somewhat increasing order of complexity.
Fuzzy Control
Table 16.1 Rule Rule Rule Rule Rule Rule Rule Rule Rule
1 2 3 4 5 6 7 8 9
683
Verbal Format of If–Then Rules If If If If If If If If If
ZðeÞ and ZðceÞ, then ZðuÞ SPðeÞ and ZðceÞ, then SNðuÞ LPðeÞ and ZðceÞ, then LNðuÞ SNðeÞ and ZðceÞ, then SPðuÞ LNðeÞ and ZðceÞ, then LPðuÞ SPðeÞ and SNðceÞ, then ZðuÞ SNðeÞ and SPðceÞ, then ZðuÞ SPðeÞ and SPðceÞ, then LNðuÞ LPðeÞ and LPðceÞ, then LNðuÞ
Example 16.8.1 Consider a simple one rule fuzzy controller, having the following rule: Rule: If e1 is slow and e2 is fast, then u is medium The graphical representation of the rule involving the membership functions of the three members slow, fast, and medium is given in Figure 16.14a. Determine the fuzzy control u. Solution To determine the fuzzy control u, we distinguish the following two steps: Step 1 Consider the particular time instant k. For this time instant, let the fuzzy variable e1 have the value 25 and the fuzzy variable e2 the value 65, both on the scale 0–100. Through these points, two vertical lines are drawn, one for each column, intersecting the fuzzy sets e1 and e2 (Figure 16.14b). Each of these two intersection points (also called triggering points) has a particular , denoted as kei ; i ¼ 1; 2. This results in the following: First column (fuzzy variable e1 ): ke1 ¼ 0:5 Second column (fuzzy variable e2 ): ke2 ¼ 0:2
Figure 16.11 Graphical representation of the five fuzzy sets: zero (Z), small positive (SP), large positive (LP), small negative (SN), and large negative (LN).
684
Figure 16.12
Chapter 16
Graphical forms of rules 1–5 of Table 16.1.
Fuzzy Control
Figure 16.13
685
Graphical forms of the rules 6–9 of Table 16.1.
Next, determine the value of sk1 , defined as follows: n o sk1 ¼ min ke1 ; ke2 ¼ minf0:5; 0:2g ¼ 0:2
ð16:8-1Þ
This completes the first step, i.e., the determination of the sk1 . Note that sk1 is related only to the ‘‘if’’ part of the rule. This step is depicted in Figure 16.14b. Clearly, when e1 ; e2 ; . . . ; en variables are involved in the ‘‘if’’ part of the rule and there is a total of r rules, then n o skp ¼ min ke1 ; ke2 ; . . . ; ken ; p ¼ 1; 2; . . . ; r ð16:8-2Þ where p indicates the particular rule under consideration.
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Figure 16.14
The inference procedure for Example 16.8.1. (a) Graphical representation of membership functions; (b) determination of the triggering points and of the min ; (c) the result of the rule; (d) compact representation of the three figures (a), (b), and (c).
Step 2 The second step is the most important step in the inference engine, since it deduces the result of the rule for the particular instant of time k. One way to deduce this result is to multiply the fuzzy variable u (third column) by sk1 . The resulting curve is the fuzzy control sought, and constitutes the result of the rule. This curve is the shaded area depicted in Figure 16.14c.
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To state this procedure more formally, let 1 ðuÞ and k1 ðuÞ denote the membership functions of the given output fuzzy set u (first row, last column of Figure 16.14) and of the curve depicted in Figure 16.14c, respectively. Then, k1 ðuÞ is given by the following expression: h i
k1 ðuÞ ¼ min ke1 ; ke2 1 ðuÞ ¼ sk1 1 ðuÞ ð16:8-3Þ For the general case, where n variables are involved in the ‘‘if’’ part of the rule and there is a total of r rules, we have the following expression: h
i p ¼ 1; 2; . . . ; r ð16:8-4Þ kp ðuÞ ¼ min ke1 ; . . . ; ken p ðuÞ ¼ skp p ðuÞ ; where p indicates the particular rule under consideration. In practice, the above two steps are presented compactly, as shown in Figure 16.14d. Clearly, the two steps are repeated for all desirable instants of time k in order to construct u for the particular time interval of interest. Example 16.8.2 Consider a fuzzy controller that is to apply a control strategy described by the following three if–then rules: Rule 1: If e1 is negative and e2 is negative, then u is negative Rule 2: if e1 is zero and e2 is zero, then u is zero Rule 3: If e1 is positive and e2 is positive, then u is positive. The graphical representation of the three rules involving the membership functions of the three fuzzy members negative, zero, and positive is given in Figure 16.15. (To facilitate the presentation of the method, the members positive and negative are actually crisp. A fuzzy presentation is given in Figure 16.24 of Problem 2 of Sec. 16.12.). Determine the fuzzy control u. Solution Making use of the results of Example 16.8.1, we carry out the first two steps, as follows: Step 1 Consider the particular time instant k. For this time instant, let the fuzzy variable e1 have the value 25 and the fuzzy variable e2 also have the value 25, all in the scale 0– 100. Through these points, two vertical lines are drawn, one for each column. These vertical lines intersect the fuzzy curves at different triggering points, having a particular . This results in the following: First column: in rule 1, ke1 ¼ 1; in rule 2, ke1 ¼ 0:5; and in rule 3, ke1 ¼ 0 Second column: in rule 1, ke2 ¼ 1; in rule 2, ke2 ¼ 0:5; and in rule 3, ke2 ¼ 0 Next, determine, skp , p ¼ 1, 2, 3, using definition (16.8-2) to yield:
For rule 1: sk1 ¼ min ke1 ; ke2 ¼ minf1; 1g ¼ 1
For rule 2: sk2 ¼ min ke1 ; ke2 ¼ minf0:5; 0:5g ¼ 0:5
For rule 3: sk3 ¼ min ke1 ; ke2 ¼ minf0; 0g ¼ 0
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Figure 16.15
The inference procedure for Example 16.8.2. (a) Graphical representation of rule 1; (b) graphical representation of rule 2; (c) graphical representation of rule 3.
It is evident that only rules 1 and 2 have nonzero contribution on u, while rule 3 plays no part in the final value of u. Furthermore, rule 1 is seen to be dominant, while rule 2 plays a seconday role. Step 2 Now multiply skp of each rule with the corresponding curve of the third column, using definition (16.8-4). The result of this product is a curve (shaded area) for each of the three variables shown in the third column. Since the present example involves more than one rule, as compared with Example 16.8.1, the following extra step is needed.
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Step 3 The above procedure is the implementation of each one of the rules 1, 2, and 3 via their corresponding fuzzy curves of each of the elements appearing in each rule. The next step is to take the result of each rule, which is the shaded area in the third column, and ‘‘unite’’ them together, as shown in Figure 16.16a. A popular method to construct the shaded area of Figure 16.16a is as follows. We take the union of the three shaded areas of the third column of Figure 16.15. Then, the actual control of u is the set (envelope) of these ‘‘united’’ (superimposed) areas, shown in Figure 16.16a. Strictly speaking, the term ‘‘union’’ refers to two or more sets and is defined as the maximum of the corresponding values of the membership functions. More specifically, if we let k ðuÞ be the membership function of the fuzzy control u depicted in Figure 16.16, then k ðuÞ is evaluated as follows: n o k ðuÞ ¼ max k1 ðuÞ; k2 ðuÞ; k3 ðuÞ ð16:8-5Þ where k1 ðuÞ, k2 ðuÞ, and k3 ðuÞ are the membership functions of the result of each rule. For the general case of r rules , Eq. (16.8-5) becomes n o k ðuÞ ¼ max k1 ðuÞ; . . . ; kr ðuÞ ð16:8-6Þ Now consider another instant of time k: let both fuzzy variables in the first and second column of Figure 16.15 have the value 75 on a scale from 0 to 100. Then, following the same procedure, one may similarly construct the corresponding third column of Figure 16.15. The resulting u for this second case is the shaded area in Figure 16.16b. Example 16.8.3 Consider a fuzzy controller that is to apply a control strategy described by the following two if–then rules: Rule 1: If e1 is positive and e2 is zero, then u is negative. Rule 2: If e1 is zero and e2 is zero, then u is zero.
Figure 16.16 Fuzzy control signal graphical construction for Example 16.8.2. (a) Actual control signal u when triggering at 25; (b) actual control signal u when triggering at 75.
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Chapter 16
The graphical representation of the two rules involving the membership functions of the three fuzzy members positive, zero, and negative is given in Figure 16.17. Determine the fuzzy control u. Solution Using the results of examples 16.8.1 and 16.8.2, we have the following steps. Step 1 Consider the particular time instant k. For this time instant, let the fuzzy variable e1 have the value of 60 and the fuzzy variable e2 have the value of 25. Through these points, two vertical lines are drawn, one for each column. These vertical lines intersect the fuzzy curves at different triggering points, having a particular . This results in the following: First column: in rule 1, ke1 ¼ 0:75 and in rule 2, ke1 ¼ 0:5 Second column: in rule 1, ke2 ¼ 0:4 and in rule 2, ke2 ¼ 0:5 Next, determine skp , p ¼ 1, 2, using definition (16.8-2) to yield:
For rule 1: sk1 ¼ min ke1 ; ke2 ¼ minð0:75; 0:4g ¼ 0:4
For rule 2: sk2 ¼ min ke1 ; ke2 ¼ minf0:5; 0:5g ¼ 0:5 Step 2 Multiply skp of each rule with the corresponding curve of the third column, according to defintion (16.8-4). The resulting curves are the fuzzy curves of the output u (third column, shaded areas).
Figure 16.17
The inference procedure for Example 16.8.3.
Fuzzy Control
Figure 16.18
691
Fuzzy control signal graphical construction for Example 16.8.3.
Step 3 Using definition (16.8-6), the envelope of the actual fuzzy control u is constructed by superimposing the two shaded areas of the third column to yield the curve shown in Figure 16.18. Remark 16.8.1 We may now make the following remark, regarding the overall philosophy of an FLC. To estimate the fuzzy control signal at each instant of time k, the FLC works as follows. Each rule contributres an ‘‘area’’ (i.e., the shaded areas in the last column in Figure 16.14 or 16.15 or 16.17). This area describes the output u of the controller as a fuzzy set. All these areas are subsequently superimposed in the manner explained above (see Figure 16.16 or 16.18), to give the fuzzy set of the ouput u. The envelope of this total area is the final conclusion of the interference engine for the instant of time k, deduced using the rule base. One can conclude, therefore, that the end product of the inference engine, given in Figure 16.16 or 16.18, is a rule base result, where all rules are simultaneously taken into consideration. The very last action in an FLC is, by using Figure 16.16 or 16.18, to determine the crisp values for the control signal, which will serve as an input to the process. This is defuzzification, which is explained below.
16.9
DEFUZZIFICATION
There are several methods for defuzzification. A rather simple method is the center of area method, which is defined as follows: X
ðxi Þxi i X u¼ ð16:9-1Þ
ðxi Þ i
or
692
Chapter 16
Figure 16.19
Defuzzified value of control signal.
Ð
ðxÞd dx u¼ Ð
ðxÞ dx
ð16:9-2Þ
where u is the crisp function sought, xi is the member of the set, and ðxi Þ is the associated membership function. Clearly, expressions (16.9-1) and (16.9-2) correspond to the discrete- and continuous-time cases, respectively. Example 16.9.1 Consider the shaded area in Figure 16.16a. Calculate the center of area of this shaded area using Eq. (16.9-1). Solution We have: X
ðxi Þxi ¼ The shaded area in Figure 16.16a
i
X
¼ ð50Þ þ ð0:5Þð0:5Þð50Þ ¼ 50 þ 12:5 ¼ 62:5
ðxi Þ ¼ 1 þ 0:5 ¼ 1:5
i
Hence u ¼ 62:5=1:5 ¼ 41:66. Therefore, the defuzzification procedure yields the crisp value of u, which is depicted in Figure 16.19. There are other types of defuzzification, such as the mean of maximum, first of maxima, last of maxima, etc. For more information on these techniques see [9, 20]. 16.10
PERFORMANCE ASSESSMENT
Up to now, no systematic procedures for the design of an FLC have been proposed (such as root locus, Nyquist plots, pole placement, stability tests, etc.). The basic difficulty in developing such procedures is the fact that the rule base has no mathematical description. As a consequence, it is not obvious how the rules and gains affect the overall performance of the closed-loop system.
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The problem of stability of a closed-loop system incorporating an FLC essentially remains an unsolved question, even though increasing research results have appeared recently in the literature. For linear time-variant systems with a known transfer function or state-space model, if the describing function approach is applied and together with the Nyquist plot, one may reach some safe conclusions regarding the stability margins of the systems. To evaluate the performance of FLCs a theoretical approach has been proposed, which yields a partial evaluation performance. This approach refers to the integrity of the rule base and aims at securing the accuracy of the rule base. One way to investigate integrity is to plot the input and output signal of the FLC controller. Comparison of those two waveforms provides some idea of the integrity of the rule base. Clearly, the objective of this investigation is to study the behavior of the control system and, if it is not satisfactory, to suggest improvements. 16.11
APPLICATION EXAMPLE: KILN CONTROL
The kilning process in the manufacturing of cement has attracted much attention from the control viewpoint and, indeed, was one of the first applications of fuzzy control in the process industry [3, 19, 25]. The kilning process is one of the most complex industrial processes to control and has, until the advent of fuzzy control, defied automatic control; However, human operators can successfully control this process using rules, the result of years of experience. These rules now form the basis for fuzzy control, and there are many successful applications worldwide. Briefly, in the kilning process a blend of finely ground raw materials is fed into the upper end of a long, inclining, rotating cylinder and slowly flows to the lower end, while undergoing chemical transformation due to the high temperatures produced by a flame at the lower end. The resultant product, clinker, constitutes the major component of cement. A measure of the burning zone temperature at the lower end of the rotary kiln can be obtained indirectly by measuring the torque of the motor rotating the kiln, whereas a measure of the quality of the end product is its free lime content (FCAO). These two quantities (or process output measurements) are essential in specifying the fuel feed to the kiln (i.e., the control strategy). The block diagram of Figure 16.20 is a simplified controller for the kilning process. There are two inputs e1 and e2 and one output u, defined as follows [25]: e1 ¼ change in kiln torque drive (DELTQUE or TQUE) e2 ¼ free lime content (FCAO) u ¼ output fuel rate (DELFUEL or FUEL)
Figure 16.20
Simplified controller for the kilning process.
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Chapter 16
where stands for change. The corresponding ranges of e1 , e2 , and u are ð3; 0; 3Þ, ð0:3; 0:9; 1:5Þ, and ð0:2; 0; 0:2Þ, respectively, where the middle number indicates the center of the fuzzy membership function. The rule base is composed of nine if–then rules, as shown in Table 16.2. The graphical representation of this rule base is shown in Figures 16.21 and 16.22, where each row represents a rule. The first column represents the membership function for the first input e1 ¼ change in kiln drive torque (DELTQUE or TQUE), the second column the membership function of the second input e2 ¼ free lime content (FCAO), and the third column the membership function of the output u ¼ output fuel rate (DELFUEL or FUEL). Clearly, in this example we have three sets with their corresponding members as follows: First set: TQUE (ZERO (ZE), NEGATIVE (NE), POSITIVE (PO)) Second set: FCAO (LOW (LO), OK (OK), HIGH (HI)) Third set: FUEL (LARGE POSITIVE (LP), MEDIUM POSITIVE (MP), SMALL POSITIVE (SP), NO CHANGE (NC), SMALL NEGATIVE (SN), MEDIUM NEGATIVE (MN), LARGE NEGATIVE (LN)). To determine the fuzzy controller output at some particular instant k, assume that DELTQUE and FCAO are e1 ¼ 1:2%=hr and e2 ¼ 0:54%/hr, respectively. Thus for the first controller input variable e1 (corresponding to the first column) a vertical line centered at 1:2%=hr is drawn to intercept the fuzzy sets for the change in kiln drive torque for every rule. Likewise a vertical line, centered at 0.54%/hr, is drawn to intercept the fuzzy sets for the second input e2 , free lime, for every rule. To obtain the final fuzzy output u of the controller, for this particular instant of time k, we follow the procedure presented in the examples of Sec. 16.8. We have:
Table 16.2
The Nine If–Then Rules for the Kiln Process FLC
Rule 1
If DELTQUE is zero and FCAO is low, then DELFUEL is medium negative
Rule 2
If DELTQUE is zero and FCAO is OK, then DELFUEL is zero
Rule 3
If DELTQUE is zero and FCAO is high, then DELFUEL is medium positive
Rule 4
If DELTQUE is negative and FCAO is low, then DELFUEL is small positive
Rule 5
If DELTQUE is negative and FCAO is OK, then DELFUEL is medium positive
Rule 6
If DELTQUE is negative and FCAO is high, then DELFUEL is large positive
Rule 7
If DELTQUE is positive and FCAO is low, then DELFUEL is large negative
Rule 8
If DELTQUE is positive and FCAO is OK, then DELFUEL is medium negative
Rule 9
If DELTQUE is positive and FCAO is high, then DELFUEL is small negative
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Figure 16.21 Graphical representation of the fuzzy logic interpretation of the control rules 1–5 (Table 16.2) for the kilning process.
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Figure 16.22
Graphical representation of the fuzzy logic interpetation of the control rules 6–9 (Table 16.2) for the kilning process. (Reproduced by the kind permission of FLS Automation, Denmark.)
Step 1 Using the relationship (16.8-2) for the case of a two-input controller with nine rules at the kth time instant, we have that for each rule n o n o skp ¼ min ke1 ; ke2 ¼ min kDLTQUE ; kFCAO
ð16:11-1Þ
As a result, the set of minima at this time instant, which is a measure of the strength or contribution of each rule on the final decision, is
sk1 ; sk2 ; sk3 ; sk4 ; sk5 ; sk6 ; sk7 ; sk8 ; sk9 ¼ f0:98; 0:63; 0; 0:29; 0:29; 0; 0; 0; 0g
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It is evident here that only rules 1, 2, 4, and 5 have a nonzero contribution, the remainder playing no part in the final decision. Furthermore, rule 1 is seen to be dominant, while rule 2 has a significant contribution. In contrast, rules 4 and 5 have only a small contribution. Step 2 To determine the contribution to u of each rule, i.e., kp ðuÞ; p ¼ 1; 2; . . . ; 9, we apply relation (16.8-4), i.e., the relation
kp ðuÞ ¼ skp p ðuÞ
to
determine ð16:11-2Þ
As a result, the nine curves in the third column of Figures 16.21 and 16.22 are produced. Step 3 To determine the final fuzzy control u, simultaneously taking into account all nine rules, we make use of Eq. (16.8-6) to yield
k ðuÞ ¼ max k1 ðuÞ; . . . ; k9 ðuÞ
ð16:11-3Þ
The fuzzy set k ðuÞ is given in Figure 16.23. Finally, we defuzzify k ðuÞ by obtaining the centroid of this resultant output fuzzy set k ðuÞ. The final crisp output to the fuel actuator at this sampling instant is the center of the area (COA) of the envelope of the resultant ouput fuzzy set k ðuÞ, and is calculated to be (see Figure 16.23) FUELðkÞ ¼ 0:048 m3 =hr It is clear that this procedure must be repeated at every sampling instant k. The sequence of these control decisions is then the desired rule-based control strategy. For more details see [3, 25].
Figure 16.23
The graphical representation of the control output u.
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PROBLEMS 1. 2.
3. 4.
In Example 16.8.2, construct the curves of the third column for the case k ¼ k2 , where the fuzzy variables have the value 75. Solve Example 16.8.2 when the graphical representation of the membership functions of the three fuzzy members positive, zero, and negative are as in Figure 16.24. Solve Example 16.8.3 when the graphical representation of the membership functions of the three fuzzy positive, zero, negative are as in Figure 16.25. A fuzzy controller is to apply a control strategy described by the following three if–then rules: Rule 1: If the temperature is low and the pressure is zero, then the speed is low. Rule 2: If the temperature is medium and the pressure is low, then the speed is medium. Rule 3: If the temperature is high and the pressure is high, then the speed is high. The ranges of the variables are: temperature of 0 to 1008C, pressure from 0 to 10 lb, and the speed from 0 to 100 m/sec. (a)
Describe the temperature, pressure, and speed by graphical representation as fuzzy sets. (b) Determine the three rules using the above fuzzy sets. (c) For the instant of time k, the values of temperature and pressure are 308C and 5 lb, respectively. Determine the fuzzy output (speed) set. (d) Determine the crisp value by defuzzifying the above fuzzy output set.
Figure 16.24
The membership functions for Problem 2.
Figure 16.25
The membership functions for Problem 3.
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BIBLIOGRAPHY Books 1. 2. 3.
4.
5.
6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
PJ Antsaklis, KM Passino (eds). An Introduction to Intelligent and Autonomous Control. Boston: Kluwer, 1992. D Driankov, H Hellendoorn, M Reinfrank. An Introduction to Fuzzy Control. Berlin: Springer-Verlag, 1993. LP Homblad, JJ Ostergaard. Control of a cement kiln by fuzzy logic. In: MM Gupta and Sanchez, eds. Fuzzy Information and Decision Processes. Amsterdam: North-Holland, 1982. JJ Ostergaard. Fuzzy logic control of a heat exchanger process. In: MM Gupta, GN Saridis, BR Gaines, eds. Fuzzy Automata and Decision Processes. Amsterdam: NorthHolland, 1977. M Mizumoto, S Fukami, K Tanaka. Some methods of fuzzy reasoning. In: MM Gupta, RK Ragade, RR Yager, eds. Advances in Fuzzy Set Theory Applications. New York: North Holland, 1979. CJ Harris, CG Moore, M Brown. Intelligent Control: Aspects of Fuzzy Logic and Neural Nets. London: World Scientific, 1990. A Kaufman. Introduction to the Theory of Fuzzy Sets. New York: Academic Press, 1975. RE King. Computational Intelligence in Control Engineering. New York: Marcel Dekker, 1999. B Kosko. Neural Networks and Fuzzy Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1992. PM Larsen. Industrial application of fuzzy logic control. In: EH Mamdani, BR Gains, eds. Fuzzy Reasoning and Its Applications. London: Academic Press, 1981. PN Paraskevopoulos. Digital Control Systems. London: Prentice Hall, 1996. W Pedrycz. Fuzzy Control and Fuzzy Systems. New York: John Wiley & Sons, 1993. GN Saridis. Self-Organizing Control of Stochastic Systems. New York: Marcel Dekker, 1977. M Sugeno (ed.). Industrial Applications of Fuzzy Control. Amsterdam: North Holland, 1985. RR Yager, S Ovchinnikov, RM Tong, HT Nguyen. Fuzzy Sets & Applications: Selected Papers by LA Zadeh. New York: John Wiley & Sons, 1987. HJ Zimmerman. Fuzzy Set Theory and Its Applications. Boston: Kluwer, 1993.
Articles 17. 18. 19. 20. 21. 22. 23.
S Assilian, EH Mamdani. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Machine Studies 7:1–13, 1974. WJM Kickert, HR van Nauta Lemke. Application of a fuzzy controller in a warm water plant. Automatica 12:301–308, 1976. RE King. Expert supervision and control of a large-scale plant. J Intelligent Systems and Robotics 5:167–176, 1992. CC Lee. Fuzzy logic in control systems: fuzzy logic controller. IEEE Trans on Systems, Man and Cybernetics 20:404–435, 1990. EH Mamdani. Application of fuzzy algorithms for the control of a simple dynamic plant. Proc IEE 121:1585–1588, 1974. EH Mamdani. Application of fuzzy logic to approximate reasoning. IEEE Trans Computers 26:1182–1191, 1977. NJ Mandic, EM Scharf, EH Mamdani. Practical application of a heuristic fuzzy rulebased controller to the dynamic control of a robot arm. IEE Proc D 132:190–203, 1985.
700 24. 25. 26. 27. 28. 29. 30. 31. 32.
33. 34. 35. 36.
Chapter 16 S Murakami, F Takemoto, H Fulimura, E Ide. Weldline tracking control of arc welding robot using fuzzy logic controller. Fuzzy Sets and Systems 32:221–237, 1989. JJ Ostergaard. FUZZYII: the new generation of high level kiln control. Zement Kalk Gips (Cement-Lime-Gypsum) 43:539–541, 1990. TJ Procyk, EH Mamdani. A linguistic self-organizing process controller. Automatica 15:15–30, 1979. M Sugeno, T Murofushi, T Mori, T Tatematsu, J Tanaka. Fuzzy algorithmic control of a model car by oral instructions. Fuzzy Sets and Systems 32:207–219, 1989. KL Tang, RJ Mulholland. Comparing fuzzy logic with classical controller design. IEEE Trans Systems Man and Cybernetics 17:1085–1087, 1987. SG Tzafestas. Fuzzy systems and fuzzy expert control: an overview. The Knowledge Engineering Review 9:229–268, 1994. BAM Wakileh, KF Gill. Use of fuzzy logic in robotics. Computers in Industry 10:35–46, 1988. T Yamakawa, T Miki. The current mode fuzzy logic integrated circuits fabricated by the standard CMOS process. IEEE Trans Computers 35:161–167, 1986. S Yasunobu, S Miyamoto, H Ihara. Fuzzy control for automatic train operation system. Proceedings IFAC/IFIP/IFORS Int Congress on Control in Transportation Systems, Baden-Baden, 1983. LA Zadeh. Fuzzy sets. Information and Control 8:338–353, 1965. LA Zadeh. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans SMC 3:43–80, 1973. LA Zadeh. Making computer think like people. IEEE Spectrum pp 26–32, 1984. LA Zadeh. The concept of a linguistic variable and its application to approximate reasoning. Information Sci 8:43–80, 1975.
Appendix A Laplace Transform Tables Table A.1
Laplace Transform Properties and Theorems f ðtÞ
Properties or theorems
FðsÞ ð1
1 Definition of the Laplace transform
f ðtÞ
2 Definition of the inverse Laplace transform
1 2j
3 Linearity
c1 f1 ðtÞ þ c2 f2 ðtÞ
c1 F1 ðsÞ þ c2 F2 ðsÞ
4 First derivative
df ðtÞ dt
sFðsÞ f ð0Þ
d2 f ðtÞ dt2
s2 FðsÞ sf ð0Þ f ð1Þ ð0Þ
5 Second derivative
6 nth derivative 7 Integral
ð cþj1 FðsÞest ds
sn FðsÞ sn1 f ð0Þ f ðn1Þ ð0Þ FðsÞ s
0
9 Double integral
1
ðt
1
ðt 10 nth time integral
f ðtÞðdtÞ2
1
ðt
f ðtÞðdtÞ 1 ffl{zfflfflfflfflffl1 |fflfflfflfflffl ffl} n times
11 Time scaling
FðsÞ f ð1Þ ð0Þ þ s s
f ðtÞ dt
ðt
FðsÞ
cj!
dn f ðtÞ dtn ðt f ðtÞ dt ðt
8 Integral
f ðtÞest dt
0
f ðatÞ
n
FðsÞ f ð1Þ ð0Þ f ð2Þ ð0Þ þ þ s s2 s2 FðsÞ f ð1Þ ð0Þ f ð2Þ ð0Þ þ þ n1 þ sn sn s ðnÞ f ð0Þ þ s 1 s F a a (continued) 701
702
Table A.1
Appendix A (continued) f ðtÞ
Properties or theorems
FðsÞ
12 Shift in the frequency domain
eat f ðtÞ
Fðs þ aÞ
13 Shift in the time domain
f ðt aÞuðt aÞ
eat FðsÞ
14 Multiplication of a function tf ðtÞ by t 15 Division of a function by t
d FðsÞ ds
ð1
f ðtÞ t
FðaÞ da s
16 Multiplication of a function n t f ðtÞ by tn f ðtÞ 17 Division of a function by tn tn
dn ð1Þn n FðsÞ ðds ðt t FðsÞðdsÞn s ffl{zfflfflfflffl}s |fflfflffl n times
ðt 18 Convolution
hðt ÞuðÞd
HðsÞUðsÞ
0
19 The initial value theorem 20 The final value theorem
lim f ðtÞ
lim sFðsÞ
t!0
t!1
lim f ðtÞ
lim sFðsÞ
t!1
t!0
Remark A.1.1 In the properties 8, 9, and 10, the constant f ðkÞ ð0Þ is defined as follows: ð0 ð0 ð0 f ð1Þ ð0Þ ¼ fðtÞ dt; f ð2Þ ð0Þ ¼ fðtÞdt2 ; etc. 1
1
1
Laplace Transform Tables
Table A.2
703
Laplace Transform Pairs f ðtÞ ¼ L1 ½FðsÞ
SN FðsÞ ¼ L½ f ðtÞ 1
1
ðtÞ
2
s
ð1Þ ðtÞ ðnÞ ðtÞ
n
3
s
4
1 s
uðtÞ
5
1 s2
t
6
1 sn
tn1 ðn 1Þ!
7
1 s1=2
1 ðtÞ1=2
8
snþ1=2
2n tn1=2 1 3 5 ð2n 1Þ 1=2
9
1 sþa
eat
10
1 ðs þ aÞ2
teat
11
1 ðs þ aÞn
tn1 eat ðn 1Þ!
12
1 s2 þ a2
1 sin at a
13
1 ðs2 þ a2 Þ2
1 ðsin at at cos atÞ 2a3
14
1 s2 a2
1 sinh at a
15
1 ðs þ aÞðs þ bÞ
eat ebt ba
16
1 ðc bÞeat ða cÞebt ðb aÞect ðs þ aÞðs þ bÞðs þ cÞ ðb aÞðc bÞða cÞ
17
ðs þ aÞ ðs þ bÞðs þ cÞ
ða bÞebt ða cÞect ðc bÞ
18
s s2 þ a2
cos at
1
Remarks
n is a positive integer
n is a positive integer
n is a positive integer
n is a positive integer
(continued)
704
Appendix A
Table A.2
(continued) f ðtÞ ¼ L1 ½FðsÞ
SN FðsÞ ¼ L½ f ðtÞ 19
s 2 s a2
cosh at
20
sþa ðs þ aÞ2 þ b2
eat cos bt
21
b ðs þ aÞ2 þ b2
eat sin bt
22
1 s2 ðs þ aÞ
1 at ðe þ at 1Þ a2
23
1 ðs þ aÞ2 ðs þ bÞ
1 ½ðb aÞt 1eat þ ebt 2 ðb aÞ
24
1 sðs2 þ a2 Þ
1 ð1 cos atÞ a2
25
s ðs þ aÞðs þ bÞ
1 ðbebt aeat Þ ba
26
1 sðs þ aÞ2
1 ð1 ðat þ 1Þeat Þ a2
27
1 sðs þ aÞðs þ bÞ
beat aebt 1 þ abðb aÞ ab
28
1 s ðs þ a2 Þ
1 ðat sin atÞ a3
29
1 s4 a4
30
sþb s2 þ a2
1 ðsinh at sin atÞ 2a3 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi a2 þ b2 sinðat þ Þ a
31
ðs2
32
s2 ðs2 þ a2 Þ2
1 ðat cos at þ sin atÞ 2a
33
sn ðs2 þ a2 Þnþ1
tn sin at n!2n a
34
sþb sðs þ aÞ2
b ab b at þ e t a a2 a2
35
sþc sðs þ aÞðs þ bÞ
c a at c b bt c e þ e þ aða bÞ bðb aÞ ab
2
2
s þ a2 Þ2
t sin at 2a
Remarks
¼ tan1
a b
Laplace Transform Tables
Table A.2
705
(continued) f ðtÞ ¼ L1 ½FðsÞ
SN FðsÞ ¼ L½ f ðtÞ 2
36
s b ðs2 þ b2 Þ2
t cos bt
37
s ðs2 þ a2 Þðs2 þ b2 Þ
cos at cos bt b2 a2
38
s ðs þ aÞðs þ bÞðs þ cÞ
2
39
s ðs þ aÞðs þ bÞðs þ cÞ
a 6¼ b
aeat bebt ðb aÞðc aÞ ða bÞðc bÞ cect ða cÞðb cÞ
a2 eat b2 ebt þ ðb aÞðc aÞ ða bÞðc bÞ þ
c2 ect ða cÞðb cÞ
40
s ðs þ aÞðs þ bÞ2
41
s2 ðs þ aÞðs þ bÞ2
42
1 ðs þ aÞðs2 þ bÞ2
a2 eat þ ðb2 ða bÞt þ b2 2abÞebt ða bÞ2 1 1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi at 2 2 e a þ b sinðbt þ Þ b a2 þ b2
43
s ðs þ aÞðs2 þ b2 Þ
1 1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi at 2 2 sinðbt þ Þ e þ b a a a2 þ b2
44
s2 ðs þ aÞðs2 þ b2 Þ
45
1 s½ðs þ aÞ2 þ b2
a2 b pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi at 2 þ b2 sinðbt Þ a e a2 a2 þ b2 1 b pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 þ b2 eat sinðbt þ Þ a 1 a a2 þ b2
46
1 s2 ½ðs þ aÞ2 þ b2
1 2a 1 at t þ sinðbt þ Þ e a2 þ b2 a2 þ b2 b
47
1 sðs2 þ a2 Þ2
1 1 ð1 cos atÞ 3 t sin at a4 2a
48
1 s4 a4
1 ðcosh at cos atÞ 2a2
49
s4
s2 a4
Remarks
2
aeat þ ðbða bÞt aÞebt ða bÞ2
¼ tan1 ¼ tan1 ¼ tan1 ¼ tan1 ¼ tan1
a b a b a b a b a b
1 ðsinh at þ sin atÞ 2a
(continued)
706
Appendix A
Table A.2
(continued) f ðtÞ ¼ L1 ½FðsÞ
SN FðsÞ ¼ L½ f ðtÞ 50
1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 s þ a2
J0 ðatÞ
51
1 s ln b
bt
52
ln
sþa sþb
1 bt ðe eat Þ t
Remarks J0 : Bessel function
Appendix B The Z-Transform
B.1
INTRODUCTION
It is well known that the Laplace transform is a mathematical tool which facilitates the study and the design of linear time-invariant continuous-time control systems (see Chap. 2). The reason for this is that it transforms the differential equation which describes the system under control to an algebraic equation. The corresponding technique for the discrete-time systems is the Z-transform. Indeed, the Z-transform facilitates significantly the study and the design of linear time-invariant discrete-time systems since it transforms the difference equation which describes the system under control to an algebraic equation. Since the study of an algebraic equation is much easier than that of a difference equation, the Z-transform has been extensively used as a basic study and design tool for discrete-time systems. This appendix is devoted to the Z-transform, covering the basic theory together with several examples. More specifically, we begin the appendix with the definitions of certain basic discrete-time signals. Subsequently, we present the definitions and some basic properties and theorems of the Z-transform. Finally, the inverse Z-transform is defined and some illustrative examples are presented. B.2
THE BASIC DISCRETE-TIME CONTROL SIGNALS
In this section we present the definitions of the following basic discrete-time signals: the unit pulse sequence, the unit step sequence, the unit gate sequence, the ramp sequence, the exponential sequence, the alternating sequence, and the sine sequence. These signals are very important for control applications. 1 The Unit Pulse Sequence The unit pulse sequence is designated by ðk k0 Þ and is defined as follows: 1; for k ¼ k0 ðk k0 Þ ¼ ðB:2-1Þ 0; for k 6¼ k0 The graphical representation of ðk k0 Þ is given in Figure B.1. 707
708
Figure B.1
Appendix B
The unit pulse sequence ðk k0 Þ.
2 The Unit Step Sequence The unit step sequence is designated by ðk k0 Þ and is defined as follows: 1; for k k0 ðB:2-2Þ ðk k0 Þ ¼ 0; for k < k0 The graphical representation of ðk k0 Þ is given in Figure B.2. 3 The Unit Gate Sequence The unit gate sequence is designated by g ðkÞ ¼ ðk k1 Þ ðk k2 Þ and is defined as follows: 1; for k1 k k2 ðB:2-3Þ g ðkÞ ¼ 0; for k < k1 and for k > k2 Figure B.3 shows the graphical representation of g ðkÞ. The unit gate sequence is usually used to zero all values of another sequence outside a certain time interval. Consider, for example, the sequence f ðkÞ. Then, the sequence yðkÞ ¼ f ðkÞg ðkÞ becomes f ðkÞ; for k1 k k2 yðkÞ ¼ f ðkÞg ðkÞ ¼ 0; for k < k1 and for k > k2 4 The Ramp Sequence The ramp sequence is designated by rðk k0 Þ and is defined as follows: k k0 ; for k k0 rðk k0 Þ ¼ 0; for k < k0
Figure B.2
The unit step sequence ðk k0 Þ.
ðB:2-4Þ
The Z-Transform
Figure B.3
709
The unit gate sequence g ðkÞ ¼ ðk k1 Þ ðk k2 Þ.
Figure B.4 shows the graphical representation of rðk k0 Þ. 5 The Exponential Sequence The exponential sequence is designated by gðkÞ and is defined as follows: k a ; for k 0 gðkÞ ¼ 0; for k < 0
ðB:2-5Þ
Figure B.5 shows the graphical representation of gðkÞ ¼ ak . Clearly, when a > 1 the values of gðkÞ increase as k increases, whereas for a < 1 the values of gðkÞ decrease as k decreases. For a ¼ 1, gðkÞ remains constantly equal to 1. In this last case, gðkÞ becomes the unit step sequence ðkÞ. 6 The Alternating Sequence The alternating sequence is designated by "ðkÞ and is defined as follows: ð1Þk ; for k k0 "ðkÞ ¼ 0; for k < k0
ðB:2-6Þ
Figure B.6 shows the graphical representation of "ðkÞ. 7 The Sine Sequence The sine sequence is defined as follows: A sin !0 k; for k 0 f ðkÞ ¼ 0; for k < 0
ðB:2-7Þ
Figure B.7 shows the graphical representation of f ðkÞ ¼ A sin !0 k, with !0 ¼ 2=12.
Figure B.4
The ramp sequence rðk k0 Þ.
710
Appendix B
Figure B.5
The exponential sequence gðkÞ ¼ ak .
Figure B.6
The alternating sequence "ðkÞ.
Figure B.7
The sine sequence f ðkÞ ¼ A sin !0 k.
The Z-Transform
B.3
711
THE Z -TRANSFORM
B.3.1 Introduction to the Z -Transform The Z-transform of a discrete-time function f ðkÞ is designated by FðzÞ and is defined as follows: FðzÞ ¼ Z½ f ðkÞ ¼
1 X
f ðkÞzk
ðB:3-1Þ
k¼1
If the discrete-time function f ðkÞ is causal, i.e., f ðkÞ ¼ 0 for k < 0, then the definition (B.3-1) becomes FðzÞ ¼ Z½ f ðkÞ ¼
1 X
f ðkÞzk
ðB:3-2Þ
k¼0
In practice, the discrete-time sequence is usually produced from a continuoustime function f ðtÞ. The conversion of f ðtÞ to f ðkTÞ, where T represents the time distance between two points of f ðkTÞ, is achieved through a sampler, as is shown in Figure B.8. The sampler is actually a switch which closes instantly and with frequency fs ¼ 1=T. The resulting output f ðkTÞ represents a discrete-time function with amplitude equal to the amplitude of f ðtÞ at the sampling instants kT, k ¼ 0; 1; 2; . . . . The following theorem refers to the criteria for choosing the sampling frequency fs ¼ 1=T (this issue was first investigated by Nyquist, but Shannon gave the complete proof of the theorem). Theorem B.3.1 Let f1 be the highest frequency in the frequency spectrum of f ðtÞ. Then, for f ðtÞ to be recovered from f ðkTÞ, it is necessary that fs 2f1 . It is noted that the function f ðtÞ may be reproduced from f ðkTÞ using a hold circuit (see Sec. 12.3) in series with a low-frequency filter which smooths out the form of the signal.
Figure B.8
The operation of a sampler.
712
Appendix B
Let T ðtÞ denote the infinite sequence of unit pulse functions (or Dirac functions) shown in Figure B.9. In addition, let f ðtÞ be the following function: 1 X
f ðtÞ ¼ f ðtÞT ðtÞ ¼
f ðkTÞðt kT Þ
T ðtÞ ¼
where
k¼1
1 X
ðt kTÞ
k¼1
ðB:3-3Þ When f ðtÞ is causal (and this is usually the case), Eq. (B.3-3) becomes f ðtÞ ¼
1 X
f ðkTÞðt kT Þ
ðB:3-4Þ
k¼0
The Laplace transform of Eq. (B.3-4) is ð1 1 1 X X F ðsÞ ¼ L f ðtÞ ¼ f ðkT Þ ðt kTÞest dt ¼ f ðkT ÞekTs 0
k¼0
ðB:3-5Þ
k¼0
The Z-transform of f ðkTÞ is FðzÞ ¼ Z½f ðkT Þ ¼
1 X
f ðkTÞzk
ðB:3-6Þ
k¼0
If we use the mapping z ¼ eTs
or
s ¼ T 1 ln z
ðB:3-7Þ
then FðzÞ ¼ F ðsÞ
ðB:3-8Þ s¼T 1 ln z
Equation (B.3-8) shows the relation between the sequence f ðkTÞ and the function f ðtÞ described in the z- and s-domain, respectively. A continuous-time system with input f ðtÞ and output f ðtÞ is called an ideal sampler (see, also, Sec. 12.3). The inverse Z-transform of a function FðzÞ is denoted as f ðkTÞ and is defined as þ 1 ðB:3-9Þ FðzÞzk1 dz f ðkTÞ ¼ Z 1 ½FðzÞ ¼ 2j Equations (B.3-6) and (B.3-8) constitute the Z-transform pair.
Figure B.9
The Dirac functions T ðtÞ.
The Z-Transform
713
B.3.2 Properties and Theorems of the Z -Transform 1 Linearity The Z-transform is linear, i.e., the following relation holds: Z½c1 f1 ðkTÞ þ c2 f2 ðkTÞ ¼ c1 Z½ f1 ðkTÞ þ c2 Z½ f2 ðkTÞ ¼ c1 F1 ðzÞ þ c2 F2 ðzÞ
ðB:3-10Þ
where c1 and c2 are constants and Fi ðzÞ ¼ Z½ fi ðkTÞ;
i ¼ 1; 2
ðB:3-11Þ
Proof Apply the Z-transform definition (B.3-5) to relation (B.3-10) to yield Z½c1 f1 ðkTÞ þ c2 f2 ðkTÞ ¼
1 X ½c1 f1 ðkTÞzk þ c2 f2 ðkTÞzk k¼0
¼ c1
1 X
f1 ðkT Þzk þ c2
k¼0
1 X
f2 ðkTÞzk
k¼0
¼ c1 F1 ðzÞ þ c2 F2 ðzÞ 2 Shift in the Time Domain The discrete-time functions f ðkT TÞ and f ðkT þ TÞ are actually the function f ðkTÞ, shifted T time units to the right and to the left, respectively. From definition (B.3-6), we have ðaÞ
Z½ f ðkT TÞ ¼
1 X k¼0
¼z
f ðkT TÞzk ¼ "
1 X
f ðmT Þzm z
m¼ 1 X
f ðmTÞz
m
þ
1 X
# f ðmTÞz
m
m¼
m¼0
¼ z FðzÞ þ
1 X
f ðmTÞzðþmÞ
ðB:3-12aÞ
m¼
ðbÞ
Z½ f ðkT TÞ ðkT TÞ ¼
1 X k¼0
¼ z ¼ z ¼ z
f ½ðk ÞT ½ðk ÞTzk "
1 X
k¼0 1 X
# f ½ðk ÞT ½ðk ÞTzðkÞ f ðmTÞ ðmTÞzm
m¼ 1 X
f ðmT Þzm
m¼0
¼ z FðzÞ
ðB:3-12bÞ
714
Appendix B
ðcÞ
Z½ f ðkT þ TÞ ¼
1 X
f ðkT þ TÞzk ¼ z
k¼0
¼ z
1 X
f ½ðk þ ÞTzðkþÞ
k¼0
1 X
f ðmT Þzm
m¼
" ¼z
" ¼z
1 X
f ðmTÞz
1 X m¼0
1 X
f ðmTÞz
FðzÞ
1 X
# f ðmTÞz
m
#
m
m¼0
" ¼z
m¼0
FðzÞ
m
# f ðkTÞz
k
ðB:3-12cÞ
k¼0
where in the last step, we have set m ¼ k. From the foregoing equations, the following special cases are obtained: Z½ f ðt þ TÞ ¼ zFðzÞ z f ð0Þ
ðB:3-13aÞ
Z½ f ðt þ 2TÞ ¼ z2 FðzÞ z2 f ð0Þ ¼ zf ðTÞ
ðB:3-13bÞ
1
Z½ f ðt TÞ ¼ z FðzÞ þ f ðTÞ
ðB:3-13cÞ
Z½ f ðt 2TÞ ¼ z2 FðzÞ þ z1 f ðTÞ þ f ð2TÞ
ðB:3-13dÞ
3 Change in the z-Scale Consider the function a t f ðtÞ. Then, according to definition (B.3-6), it follows that Z½a t f ðtÞ ¼
1 X
a kT f ðkTÞzk ¼
k¼0
1 X
f ðkTÞ½a T zk ¼ Fða T zÞ
ðB:3-14Þ
k¼0
4 The Z -Transform of a Sum Consider the finite sum k X
f ðiTÞ
i¼0
This sum represents the summation of the first k þ 1 terms of the sequence f ðkTÞ. Defining gðkTÞ ¼
k X
f ðiTÞ;
g½ðk 1ÞT ¼
i¼0
k1 X
f ðiTÞ; . . .
i¼0
the discrete-time function gðkTÞ may be described by the following difference equation: gðkT þ TÞ ¼ gðkTÞ þ f ðkT þ TÞ Applying the Z-transform to this difference equation yields z½GðzÞ gð0Þ ¼ GðzÞ þ z½FðzÞ f ð0Þ
The Z-Transform
715
where use was made of Eq. (B.3-13a). Since gð0Þ ¼ f ð0Þ, this relation becomes " # k h z i X f ðiTÞ ¼ FðzÞ ðB:3-15Þ GðzÞ ¼ Z z1 i¼0 5 Multiplication by k Consider the discrete-time function f ðkTÞ. Then, the Z-transform of the function kf ðkT Þ is Z½kf ðkTÞ ¼
1 X
kf ðkTÞzk ¼ z
k¼0
¼ z
d dz
"
1 X
f ðkTÞ½kzk1 ¼ z
k¼0 1 X
1 X k¼0
#
f ðkTÞzk ¼ z
k¼0
f ðkTÞ
dzk dz
d FðzÞ dz
ðB:3-16Þ
6 Convolution of Two Discrete-Time Functions Consider the causal discrete-time functions f ðkT Þ and hðkTÞ. The convolution between these two functions is designated by yðkT Þ ¼ f ðkTÞ hðkT Þ and is defined as follows: yðkTÞ ¼ f ðkTÞ hðkTÞ ¼
1 X
f ðiTÞhðkT iTÞ ¼
i¼0
YðzÞ ¼ Z½ yðkTÞ ¼ Z½ f ðkT Þ hðkTÞ ¼ ¼
hðiTÞ f ðkT iTÞ
i¼0
The Z-transform of the function yðkTÞ is
" 1 X 1 X
1 X
" 1 1 X X k¼0
#
# f ðiTÞhðkT iTÞ zk
i¼0
hðiTÞ f ðkT iTÞ zk
i¼0
k¼0
Reversing the summing order, we have YðzÞ ¼
1 X
hðiTÞ
i¼0
" ¼
1 X
1 X k¼0
hðiTÞz
i
i¼0
f ðkT iTÞzk ¼ #"
1 X
hðiTÞzi
i¼0
1 X
# f ðmT Þz
1 X
f ðkT iTÞzðkiÞ
k¼0
m
m¼0
Since f ðmTÞ is a causal function, i.e., f ðmT Þ ¼ 0 for m < 0, it follows that " #" # 1 1 X X i m YðzÞ ¼ hðiTÞz f ðmTÞz ¼ HðzÞFðzÞ ðB:3-17Þ i¼0
m¼0
7 Discrete-Time Periodic Functions A discrete-time function f ðkTÞ is called periodic with period p if the following relation holds: f ðkTÞ ¼ f ðkT þ pTÞ
for every k ¼ 0; 1; 2; . . .
716
Appendix B
Let F1 ðzÞ be the Z-transform of the first period of f ðkTÞ, i.e., let F1 ðzÞ ¼
p1 X
f ðkTÞzk
k¼0
Then the Z-transform of the periodic function f ðkTÞ is " # p1 X p k f ðkTÞz Z½ f ðkTÞ ¼ FðzÞ ¼ Z½ f ðkT þ pTÞ ¼ z FðzÞ k¼0
¼ z p ½FðzÞ F1 ðzÞ where relation (B.3-12c) was used. Hence p z FðzÞ ¼ p F ðzÞ z 1 1
ðB:3-18Þ
8 Initial Value Theorem The following relation holds: f ð0Þ ¼ lim FðzÞ
ðB:3-19Þ
z!1
Proof The Z-transform of f ðkTÞ may be written as FðzÞ ¼
1 X
f ðkTÞzk ¼ f ð0Þ þ f ðTÞz1 þ f ð2TÞz2 þ
k¼0
Taking the limits of both sides of the above equation, as z ! 1, we immediately arrive at the relation (B.3-19). 9 Final Value Theorem The following relation holds: lim f ðkTÞ ¼ lim ð1 z1 ÞFðzÞ
k!1
ðB:3-20Þ
z!1
under the assumption that the function ð1 z1 ÞFðzÞ does not have any poles outside or on the unit circle. Proof Consider the Z-transform of f ðkT þ TÞ f ðkTÞ: Z½ f ðkT þ TÞ f ðkTÞ ¼ lim
m!1
m X ½ f ðkT þ TÞ f ðkTÞzk k¼0
Using Eqs (B.3-6) and (B.3-13a), we obtain
The Z-Transform
717
zFðzÞ zf ð0Þ FðzÞ ¼ lim
m!1
m X ½ f ðkT þ TÞ f ðkTÞzk k¼0
or ð1 z1 ÞFðzÞ f ð0Þ ¼ lim
m!1
m X ½ f ðkT þ TÞ f ðkTÞzk1 k¼0
Taking the limits on both sides of the above equation, as z ! 1, we obtain lim ð1 z1 ÞFðzÞ f ð0Þ ¼ lim
m!1
z!1
m X ½ f ðkT þ TÞ f ðkTÞ k¼0
¼ lim ½FðTÞ f ð0Þ þ ½ f ð2TÞ f ðTÞ m!1 þ þ ½ f ðmT þ TÞ f ðmT Þ ¼ lim ½f ð0Þ þ f ðmT þ TÞ ¼ f ð0Þ þ f ð1Þ m!1
Hence lim f ðkTÞ ¼ lim ð1 z1 ÞFðzÞ z!1
k!1
All the foregoing properties and theorems are summarized in Appendix C. Example B.3.1 Find the Z-transform of the impulse sequence ðkT TÞ. Solution Using definition (B.3-6), we have Z½ðkT TÞ ¼
1 X
ðkT TÞzk ¼ z
k¼0
Example B.3.2 Find the Z-transform of the step sequence ðkT TÞ. Solution Here
Z½ ðkT TÞ ¼ z Z½ ðkTÞ ¼ z ¼ z
1 X
" ðkT Þz
k
¼z
k¼0 þ1
1 z ¼ z1 1 z1
where use was made of property (B.3-12c) and of the relation 1 X k¼0
zi ¼
1 ; 1z
jzj < 1
1 X ðz1 Þk k¼0
#
718
Appendix B
Example B.3.3 Find the Z-transform of the exponential sequence gðkTÞ ¼ akT . Solution Here Z½gðkTÞ ¼
1 X
akT zk ¼
k¼0
for
1 X ðaT z1 Þk ¼
1 z ¼ 1 aT z1 z aT
k¼0
jaz1 j < 1
Example B.3.4 Find the Z-transform of the ramp sequence rðkT TÞ: Solution Here Z½rðkT TÞ ¼ z Z½rðkTÞ ¼ z
1 X
rðkTÞzk ¼ Tz
k¼0
¼ Tz ðzÞ
1 X
kzk
k¼0
d d h z i Tzþ1 Z½ ðkTÞ ¼ Tzþ1 ¼ dz dz z 1 ðz 1Þ2
where use was made of the property (B.3-16). Example B.3.5 Find the Z-transform of the alternating sequence "ðkTÞ ¼ ð1ÞkT . Solution Here Z½"ðkT Þ ¼
1 1 X X ð1ÞkT zk ¼ ½ð1ÞT z1 k ¼ k¼0
k¼0
1 z ¼ T 1 1 ð1Þ z z ð1ÞT
Example B.3.6 Find the Z-transform of the function yðkTÞ ¼ ebkT . Solution Setting a ¼ eb in Example B.3.3, we obtain Z½ebkT ¼
z z ebT
Example B.3.7 Find the Z-transform of the functions f ðkTÞ ¼ sin !0 kT and f ðkTÞ ¼ cos !0 kT:
The Z-Transform
719
Solution Here z z ¼ z e j!0 T z cos !0 T j sin !0 T zðz cos !0 T þ j sin !0 TÞ ¼ ðz cos !0 TÞ2 þ sin2 !0 T zðz cos !0 TÞ z sin !0 T þj 2 ¼ 2 z 2z cos !0 T þ 1 z 2z cos !0 T þ 1
Z½e j!0 kT ¼
Since e j ¼ cos þ j sin , it follows that Z½cos !0 kT ¼
zðz cos !0 TÞ z 2z cos !0 T þ 1
Z½sin !0 kT ¼
z sin !0 T z2 2z cos !0 T þ 1
2
Example B.3.8 Find the Z-transform of the function f ðkTÞ ¼ ebkT sin !kT: Solution Setting a ¼ eb in Eq. (B.3-14), we have Z½ebkT f ðkTÞ ¼ FðebT zÞ Using the results of Example B.3.7, we obtain Z½ebkT sin !0 kT ¼ ¼
B.4
z2 e2bT
zebT sin !0 T 2zebT cos !0 T þ 1
zebT sin !0 T z2 2zebT cos !0 T þ e2bT
THE INVERSE Z -TRANSFORM
The determination of the inverse Z-transform (as in the case of the inverse Laplace transform) is usually based upon the expansion of a rational function FðzÞ into partial fraction expansion whose inverse transform can be directly found in the tables of the Z-transform pairs given in Appendix C. It is noted that in cases where the numerator of FðzÞ involves the term z, it is more convenient to expand into partial fraction expansion the function FðzÞ=z, instead of FðzÞ and, subsequently, determine FðzÞ from the relation z½FðzÞ=z. It is also noted that there are several other techniques for the determination of the inverse Z-transform, as for example the method of the continuous fraction expansion, the direct implementation of the definition of the inverse Z-transform given by Eq. (B.3-9), and others. The method of partial fraction expansion appears to be computationally simpler over the other methods, and for this reason it is almost always used for the determination of the inverse Z-transform.
720
Appendix B
Example B.4.1 Find the inverse Z-transform of the function FðzÞ ¼
3z ðz 1Þðz 4Þ
Solution Expanding FðzÞ=z into partial fraction expansion, we obtain FðzÞ 3 1 1 ¼ ¼ z ðz 1Þðz 4Þ ðz 1Þ ðz 4Þ and hence FðzÞ ¼
z z ðz 1Þ ðz 4Þ
From the table of the Z-transform pairs (Appendix C), we find that h z i h z i ¼ ðkTÞ and Z 1 ¼ 4k Z 1 z1 z4 where T ¼ 1. Hence f ðkTÞ ¼ Z 1 ½FðzÞ ¼ ðkT Þ 4k ¼ 1 4k Example B.4.2 Find the inverse Z-transform of the function FðzÞ ¼
zðz 4Þ ðz 2Þ2 ðz 3Þ
Solution Expanding FðzÞ=z into partial fraction expansion, we obtain FðzÞ 1 2 1 ¼ þ 2 z z 2 ðz 2Þ z3 and hence FðzÞ ¼
z 2z z þ z 2 ðz 2Þ2 z 3
Since for the case T ¼ 1 h z i Z 1 ¼ 2k ; z2
Z 1
2z ¼ k2k ; ðz 2Þ2
and
it follows that f ðkTÞ ¼ Z 1 ½FðzÞ ¼ 2k þ k2k þ 3k ¼ ðk þ 1Þ2k þ 3k
Z1
h z i ¼ 3k z3
The Z-Transform
721
Example B.4.3 Find the inverse Z-transform of the function FðzÞ ¼
2z3 þ z ðz 2Þ2 ðz 1Þ
Solution Expanding FðzÞ=z into partial fraction expansion, we obtain FðzÞ 9 1 3 ¼ þ z ðz 2Þ2 z 2 z 1 and hence FðzÞ ¼
9z z 3z þ ðz 2Þ2 z 2 z 1
Since for T ¼ 1 h z i Z 1 ¼ 2k ; z2
Z
1
z ¼ k2k1 ; ðz 2Þ2
and
Z 1
h z i ¼1 z1
it follows that f ðkTÞ ¼ Z 1 ½FðzÞ ¼ 9k2k1 2k þ 3 Example B.4.4 Find the inverse Z-transform of the function FðzÞ ¼
z2 z2 2z þ 2
Solution Examining the form of the denominator z2 2z þ 2 we observe that FðzÞ may be the Z-transform of a function of the type eakT ðc1 sin !0 kT þ c2 cos !0 kTÞ, where c1 and c2 are constants. To verify this observation, we work as follows. The constant term 2 is equal to the exponential e2aT , in which case a ¼ ðln 2Þ=2T. The coefficient 2 of thepzffiffiffi term must be equal to the function 2eaT cos !0 T, in which case cos !0 T ¼ 1= 2 and !0 ¼ =4T. Consequently, the denominator of FðzÞ can be written as follows: ln 2 ln 2 z2 2z þ 2 ¼ z2 2z eð 2T ÞT cos T þ e2ð 2T ÞT 4T The numerator can be written as z2 ¼ ðz2 zÞ þ z. Since ln 2 ln 2 T ¼ eð 2T ÞT sin T ¼1 eð 2T ÞT cos 4T 4T it follows that the numerator ðz2 zÞ þ z may be written as follows: h i ln 2 2 z2 ¼ z2 zeð 2T ÞT cos T þ zeð2T ÞT sin T 4T 4T
722
Appendix B
Hence, the function FðzÞ may finally be written as z2 z z þ z2 2z þ 2 z2 2z þ 2 ln 2 T z2 zeð 2T ÞT cos 4T ¼ ln 2 ln 2 T þ e2ð 2T ÞT z2 2zeð 2T ÞT cos 4T ln 2 T zeð 2T ÞT sin 4T þ ln 2 ln 2 T þ e2ð 2T ÞT z2 2zeð 2T ÞT cos 4T From the table of the Z-transform pairs (Appendix C), it follows that h i ln 2 f ðkTÞ ¼ Z 1 ½FðzÞ ¼ eð 2T ÞkT cos kT þ sin kT 4T 4T ln 2 k k þ sin for T ¼1 ¼ e 2 k cos 4 4 FðzÞ ¼
Appendix C Z-Transform Tables Table C.1
Properties and Theorems of the Z-Transform f ðkTÞ
Property or theorem 1 Definition of Z-transform
f ðkTÞ
FðzÞ 1 X
f ðkTÞzk
k¼0
þ
2 Definition of the inverse Z-transform
1 FðzÞzk1 dz 2j
FðzÞ
3 Linearity
c1 f1 ðkTÞ þ c2 f2 ðkTÞ
c1 F1 ðzÞ þ c2 F2 ðzÞ
4 Shift to the left (advance)
f ðkT þ TÞ
z FðzÞ
1 X
! f ðkTÞzk
k¼0
5 Shift to the right (delay)
f ðkT TÞ
z FðzÞ
6 Change in z-scale
a kT f ðkTÞ
Fða T zÞ
7 Change in kT-scale
f ðmkTÞ
Fðzm Þ
8 Multiplying by k
kf ðkTÞ
z
m X
f ðkTÞ
z FðzÞ z1
10 Convolution
f ðkTÞ hðkTÞ
FðzÞHðzÞ
11 Periodic function
f ðkTÞ ¼ f ðkT þ pTÞ
zp F ðzÞ z 1 1
12 Initial value theorem
f ð0Þ
9 Summation
d FðzÞ dz
k¼0
13 Final value theorem
lim f ðkTÞ
k!1
p
lim FðzÞ
z!1
limð1 z1 ÞFðzÞ
z!1
723
1 or z0 zaþ1 z1 z z1 Tzaþ1 ðz 1Þ2 Tz ðz 1Þ2
T 3 zðz2 þ 4z þ 1Þ 6ðz 1Þ4 ð1Þm @m z lim a!0 m! @am z eaT z z aT
1 eaTs s 1 s eaTs s2 1 s2 1 s3 1 s4 1 smþ1 1 s T ln a
ðkTÞ
ðkT aTÞ
ðkTÞ
kT aT
kT
1 2 2 k T 2!
1 2 3 k T 3!
1 m m k T m!
akT
2
3
4
5
6
7
8
9
10
T 2 zðz þ 1Þ 2ðz 1Þ3
za
f ðkTÞzk
eaTs
k¼0
1 X
ðkT aTÞ
FðzÞ ¼
1
0
f ðtÞest dt
f ðkTÞ
ð1
SN
FðsÞ ¼
Z-Transform Pairs
Table C.2
724 Appendix C
a sðs þ aÞ
1 eakT
15
cos !0 kT
sinh !0 kT
cosh !0 kT
19
20
sin !0 kT
18
17
1 eakT a
1 ðs þ aÞmþ1
km T m akT e m!
14
kT
T 2 eaT z T 2 e2aT z þ 2 aT 2ðz e Þ ðz eaT Þ3 ð1Þm @m z m! @am z eaT
1 ðs þ aÞ3
k2 T 2 akT e 2
13
16
TzeaT ðz eaT Þ2
1 ðs þ aÞ2
kTeakT
12
T 1 eaT 2 aðz 1Þðz eaT Þ ðz 1Þ z sin !0 T z2 2z cos !0 T þ 1 zðz cos !0 TÞ z2 2z cos !0 T þ 1 z sinh !0 T z2 2z cosh !0 T þ 1 zðz cosh !0 TÞ z2 2z cosh !0 T þ 1
!0 s þ !20 s s þ !20 !0 s2 !20 s s2 !20
2
2
2
1 s ðs þ aÞ
ð1 eaT Þz ðz 1Þðz eaT Þ
z z eaT
1 sþa
eakT
11
(continued)
Z-Transform Tables 725
z z aTzeaT aT z1 ze ðz eaT Þ2 bz bz aða bÞTzeaT þ z 1 z eaT ðz eaT Þ2 z z ða bÞTzeaT þ aT bT ze ze ðz eaT Þ2
a2 sðs þ aÞ2 a2 ðs þ bÞ sðs þ aÞ2 ða bÞ2 ðs þ bÞðs þ aÞ2 !0 ðs þ aÞ2 þ !20
1 ð1 þ akTÞeakT
b beakT þ aða bÞkTeakT
ebkT eakT þ ða bÞkTeakT
eakT sin !0 kT
25
26
27
28
zeaT sin !0 T z 2zeaT cos !0 T þ e2aT
ðc aÞz ðb cÞz z eaT z ebT
ðb aÞðs þ cÞ ðs þ aÞðs þ bÞ
ðc aÞeakT þ ðb cÞebkT
24
2
z z z eaT z ebT
ba ðs þ aÞðs þ bÞ
eakT ebkT
23
2
z zðz cos !0 TÞ z 1 z2 2z cos !0 T þ 1
f ðkTÞzk
!20 sðs2 þ !20 Þ
k¼0
1 X
1 cos !0 kT
FðzÞ ¼
22
f ðtÞest dt zðz cosh !0 TÞ z z2 2z cosh !0 T þ 1Þ z 1
0
ð1
!20 sðs !20 Þ
FðsÞ ¼
cosh !0 kT 1
f ðkTÞ
ðcontinuedÞ
21
Table C.2
726 Appendix C
32
31
30
29
ða2 þ !20 Þðs þ bÞ s½ðs þ aÞ2 þ !20
b beakT sec cosð!0 kT þ Þ;
where ¼ tan
1
"
# a2 þ !20 ab b!0
a where ¼ tan1 !0
ba !0 a2 þ !20 s½ðs þ aÞ2 þ !20
1 eakT sec cosð!0 kT þ Þ;
where ¼ tan1
ða bÞ2 þ !20 ðs þ bÞ½ðs þ aÞ2 þ !20
sþa ðs þ aÞ2 þ !20
ebkT eakT sec cosð!0 kT Þ;
eakT cos !0 kT
z2 zeaT cos !0 T 2zeaT cos !0 T þ e2aT
bz b½z2 zeaT sec cosð!0 T þ Þ 2 z1 z 2zeaT cos !0 T þ e2aT
z z2 zeaT sec cosð!0 T þ Þ 2 z 1 z 2zeaT cos !0 T þ e2aT
z z2 zeaT sec cosð!0 T þ Þ z ebT z2 2zeaT cos !0 T þ e2aT
z2
Z-Transform Tables 727
Index
Acceleration (or parabolic) error constant, 172 Ackermann’s formula, 440 AC motors, 114 Active circuit realization, 402 Actuator, 119 Adaptive control, 603 with the gradient method, 605 model reference, 604 direct, 605 hyperstability design, 608 indirect, 605 A/D converters, 527 Adding poles and zeros to transfer function, influence on Nyquist diagram, 326 Addition of poles and the root locus, 296 Addition of zeros and the root locus, 299 Aircraft wing control system, 13 Algebraic control, 435 Algebraic criteria, 243 Algebraic stability criteria, 245 Alternating sequence, 709 Amplitude-phase theorem, 348 Analysis problem, 5 Analytical expression of time response, 147 A posteriori filtered plant-model error, 615 A priori adaptation error, 310 Arrival points, 275 Asymptotic stability, 149, 238, 239, 262, 540
Attenuation constant, 155 Augmented error, 619 Automatic piloting system for supersonic airplanes, 285 Backward difference method, 530 Bandwidth, 307 Basic control signals, 27 Basic discrete-time control signals, 707 Basic structure of control systems, 4 Bass-Gura formula, 439 Bezout identity, 611 Bilinear transformation method, 532, 561 Block diagram, 93, 213 simplification of, 99 Block diagram rules, 94 blocks in cascade, 94 blocks in parallel, 94 construction of, 106 converting closed- to open-loop, 96 converting F(s) into unity, 97 converting open- to closed-loop, 97 definitions of, 105 moving a summation point, 98 Bode criterion, 245 Bode diagrams, 338, 563 and the transfer function, 339, 343 Bode’s amplitude-phase theorem, 348 Boiler-generator control system, 15 Boundary conditions, 483, 494 Bounded-input stability, 542 729
730 Bounded-output stability, 542 Bridged T, 381 Canonical equation, 584, 586 Canonical Hamiltonian equations, 491, 494 Causality, 518 Cayley-Hamilton theorem, 55 Certainty equivalence principle, 613, 622 Change in the z-scale, 714 Characteristic equation, 52 Characteristic polynomial, 52 invariance of, 205 Chemical composition control system, 131 Classical control, 4 design methods of, 367, 371 Classical and discrete-time controller design, 552 Classical optimal control methods, 418 Classical time-domain analysis of control systems, 147 Closed-loop system, 5, 8 design using state observers, 463 specifications, 372 Command signal, 6 Comparing algebraic criteria and the Nyquist criterion, 337 Comparing open- and closed-loop systems, 163 Compensator, 6 Completely controllable, 216 Computer-controlled system, 515 Constant amplitude loci, 351 Constant amplitude and phase loci, Nichols charts, 354 Constant phase loci, 353 Continued fraction expansion criterion, 250 Control: of economic systems, 20 of human respiratory system, 259 of large disk-storage devices, 257 of nuclear reactor, 283 of yaw in a fighter jet, 255 Controllability, 215, 216, 218, 223, 546, 548 index, 223 invariance of, 222 matrix, 216, 548 Controllability, observability, and transfer function matrix, relation between, 223
Index Controllable, 216 Controller, 6 circuits, 374 bridged T, 381 other, 381 phase-lag, 376 phase lag-lead, 378 phase-lead, 374 derivative, 568 design based on frequency response, 561 design via root locus method, 557 fixed structure, 424 FP, 678 FPD, 679 FPD+I, 679 free structure, 419 elements of, 680 fuzzy, 678 integral, 567 output feedback, 436 PD, 385 PI, 389 PID, 391, 567 active circuit realization for, 402 three-term, 568 proportional, 567 state feedback, 436 Controlling thickness of metal sheets, 289, 336 Control problem, 465 Control signal, 5, 27 Control system components, 110 Conversion of differential state equations to difference state equations, 535 Conversion of G(s) to G(z), 530 Convolution of two discrete-time functions, 715 Correlation: for first-order systems, 307 between frequency response and transient response, 307 for higher-order systems, 310 for second-order systems, 308 Cost function, 479 quadratic, 493 Critically damped, 157 D/A converters, 527 Damped natural frequency, 155 Damping constant, 155 Damping ratio, 155, 158 and overshoot, 158
Index DC motors, 110 Defuzzification, 691 interface, 681 Delay time, 154 Departure points, 275 Depth control system for submarines, 252 Description: of linear systems via state equations, 80 of linear time-invariant discrete-time systems, 518 Design: in the case of known parameters, 611 with classical optimal control methods, 418 with phase-lag controllers, 411 with phase lag-lead controllers, 415 with phase-lead controllers, 403 with PID controllers, 384 of PID controllers using Ziegler-Nichols methods, 396, 570 with proportional controllers, 382 Design methods, 367 Determination of state transition matrix, 194 Diagonal form, 205 Difference equations, 520 Differential equations, 70 Digital control, 515 Diophantine equation, 611 Direction, 106 Direct techniques, 552 Discrete-time: controller design using indirect techniques, 552 controllers derived from continuoustime controllers, 552 control signals, 707 periodic functions, 715 system, 515 description of, 516 properties of, 517 Discretized systems, 517 Distinct real roots, 39 Disturbance rejection, 177 Disturbances, 8 Division of a function by t, 38 Dominant pole, 161 Dominant pole method, 161 Dynamic equations, 79 Economic systems, 20 Effect of disturbances, 165
731 Effects of addition of poles and zeros on root locus, 296 Ending points, 275 Equilibrium point, 541 Equivalent state-space models, 92 Error constants, 171 Error detectors, 116 Estimation problem, 465 Euler-Lagrange equation, 483 Exact model matching, 454 Expert systems, 673 Exponential function, 30 Exponential sequence, 709 Fast mode, 162 Feedback, 8 Filtered error, 610 Final value theorem, 37, 716 First method of Lyapunov, 262 First-order systems, 154, 584 Forced response, 148, 198 Forgetting factor, 620, 627 Forward difference method, 532 Forward path, 106 Free response, 148, 195, 198 Frequency domain analysis, 305 Frequency response, 305 characteristics of, 307 Full-order observer, 458 Fuzzification, 681 interface, 681 Fuzzy control, 673 Fuzzy logic, 673 Fuzzy sets, 674 Gain, 106 margin, 330, 345 Gears, 117 General aspects of closed-loop control design problem, 367 Generalized node, 105 General solution of state equations, 198 Grade of membership, 675 Gradient method, 605 Hamiltonian function, 490 H1 -context, 645, 651 Heron of Alexandria, 2 Higher-order systems, 587 Historical review of automatic control, 2 Hold circuits, 528 Homogeneous solution, 148
732 Human respiratory system, 259 Human speech control, 21 Hurwitz criterion, 249 Hydraulic actuator, 119 Hydraulic servomotor, 119 Hyperstability design, unknown parameters, 613 Identification, 584, 590 Impulse response, 77, 83, 521 Indirect techniques, 552 Inference engine, 681, 682 Initial value theorem, 37, 716 Input node, 105 Input-output decoupling, 448 via output feedback, 454 via state feedback, 449 Input vector, 78 Instability, 239 Intelligent control, 673 Interval polynomials with lumped uncertainty and fixed degree, 659 Invariant impulse response method, 532 Invariant step response method, 532 Inverse multiplicative uncertainty, 649 Jordan canonical form, 54 Kalman decomposition, 227 Kalman matrix, 495 Kharitonov polynomials, 659 Kharitonov’s theorem, 659, 660 for robust stability, 659 Knowledge-based expert systems, 673 Laplace transform, 31 definition of, 32 of the derivative of a function, 33 of the integral of a function, 34 inverse, 39 pairs, 703 properties and theorems, 33, 701 tables, 701 Large disk-storage devices, 257 Laser eye surgery control system, 19 Leverrier’s algorithm, 196 Linearity, 33, 518, 713 Linear state and output feedback laws, 436 Linear time-invariant discrete-time systems, 518, 522 Linguistic variable, 677
Index Liquid-level control, 12 Liquid-level control system, 128 Loop, 106 Loss of controllability due to sampling, 551 Loss of observability due to sampling, 551 Lyapunov, 260, 263 Lyapunov criterion, 245 Lyapunov function, 263 Machine tool control system, 16, 18 Marginal stability, 239 Mason’s rule, 108 Mathematical model(s), 583 for control system components, 110 of practical control systems, 121 of systems, 67 Matrix: addition, 48 calculation of the determinant of, 50 calculation of the inversion of, 51 column vector, 47 conjugate, 48 controllability, 216, 548 definition, 46 derivatives with respect to a vector, 49 determinant of, 50 diagonal, 47 eigenvalues, 51, 52 eigenvectors, 51, 54 Hermitian, 48 identity, 47 impulse response, 83 integration, 50 inverse of, 51 inversion lemma, 591 Kalman, 495 multiplication, 49 multiplying with a constant, 49 nonsingular, 47 nonsquare, 47 observability, 220 orthogonal, 48 Riccati differential equation, 495 row vector, 47 singular, 47 square, 47 state transition, 194 symmetric, 48 transfer function, 82 invariance of, 205
Index
[Matrix] transpose of, 47, 49 triangular, 48 zero, 47 Maxima and minima: of a functional with constraints, 487 of a functional without constraints, 482 using the method of calculus of variations, 482 Maximum principle, 489, 491 Measurement problem, 5 Membership function, 676 Metal sheet thickness control system, 10 Minimum control effort problem, 480 Minimum dimension, 91 Minimum phase function, 320 Minimum time control problem, 480 Missile direction control system, 13 MIT rule, 605 Mixed node, 105 Model following, 607 Model reference adaptive control, 604 Model simplification, 159 Modern control, 4 Motor controlled by rotor, 112 Motor controlled by stator, 110 Multi-input–multi-output (MIMO) systems, 81 Multi-input–single-output (MISO) systems, 81 Multiplication of a function by t, 38 Multiplication by k, 715 Multiplicative uncertainty, 645 Natural response, 148 Natural undamped frequency, 155 Neural networks, 673 Nichols charts, 354 Nichols criterion, 245 Nichols diagrams, 351 Nominal performance, 651 Nondistinct real roots, 40 Nonminimum phase function, 320 Nuclear reactor control system, 14, 283 Numerical control tool machine, 333 Nyquist: criterion, 245, 314, 318 diagram, 318, 566 construction of, 320 for first-order systems, 321 for second-order systems, 323
733 path, 318 [Nyquist] theorem, 319 generalized, 320 Observability, 215, 219, 220, 223, 550 index, 223 invariance of, 222 Observer, 435, 457, 458, 461 Off-line identification, 584 Off-line parameter estimation, 584 On-line identification, 590 algorithm, 593 On-line parameter estimation, 590 Open-loop system, 5 Operational amplifier with resistors, 116 Optimal control, 479, 572 Optimal linear regulator, 492 Optimal regulator problem, 481 Optimal servomechanism problem, 480, 502 Optimal tracking problem, 480, 502 Order reduction, 161 Orientation control of a sun-seeker system, 17 Orientation control system, 133 Output equation, 79 Output node, 105 Output vector, 78, 79 controllability, 218 Overdamped, 158 Overshoot, 553, 158 Paper-making control system, 14 Parameter estimation, 584, 590 Parameter variations (and their effect on output): in feedback transfer function, 165 in open-loop system, 164 Partial fractions expansion, 39 Particular solution, 148 Path, 106 Perfect model following, 607 Performance index, 479 Periodic functions, 38 Persistent excitation, 627 Phase canonical form, 205, 206 formula for, 440 Phase-lag, 376 Phase-lag controllers, 411 Phase lag-lead, 378 Phase lag-lead controllers, 415
734 Phase-lead, 374 Phase-lead controllers, 403 Phase margin, 330, 331, 345 PID (proportional-integral-derivative) controllers, 384 Pneumatic amplifier, 121 Pole placement, 438 via output feedback, 447 via state feedback, 438 Pole sensitivity to parameter variations, 168 Pole-zero matching method, 532 Position control system, 9, 123, 252, 443 Position servomechanism, 9 Position (or step) error constant, 171 Potentiometer, 116 Practical control systems, 121 Primary sets, 677 Problem of minimum dimension, 91 Problem of minimum number of parameters, 91 Problem of realization, 91 Processed augmented error, 619 Properties of state transition matrix, 195 Proportional controllers, 382 Quadratic forms, 57 Ramp function, 29 Ramp sequence, 708 Rank, 48 Recursive identification, 590 Reduced-order observer, 458, 461 Reference, 6 Regression vector, 612 Regulator, 6 Regulator problem, 481 Relative stability, 314, 331 Remote robot control system, 15, 18 Residual, 459 Resonant frequency, 307 Resonant peak, 307 Response of first-order systems, 154 Response of second-order systems, 154 Riccati differential equation, 495 Rise time, 154, 554 Robots for welding, 256 Robust control, 637 Robust performance, 654 Robust performance in the H1 - context, 651 Robust stability:
Index in the H1 - context, 645 [Robust stability] with inverse multiplicative uncertainty, 649 with multiplicative uncertainty, 645 Root locus, 158, 271 construction method of, 274 angles of departure and arrival, 28 asymptotes, 276 breakaway, 279 intersection of asymptotes, 278 intersection with imaginary axis, 280 number of branches, 276 real axis segments, 279 symmetry about real axis, 276 definition of, 274 of practical control systems, 281 theorems for constructing, 275 Root locus method for determining roots of a polynomial, 294 Routh criterion, 246, 543 using Mobius transformation, 543 Rule base, 681, 682 Sampled-data systems, 517 analysis, 527, 538 description, 527 Satellite orientation control system, 133 Second method of Lyapunov, 263 Self-tuning regulators, 604, 621 explicit, 605, 622 implicit, 605, 622 pole-placement, 622 design with known parameters, 622 design with unknown parameters, 627 Sensitivity derivative, 606 Sensitivity function, 647 complementary, 647 Sensitivity to parameter variations, 166 Separation principle, 465 Servomechanism, 9 Servomechanism control systems, 123 Servomechanism problem, 480 Servomotor, 119 Settling time, 154, 155, 554 Shannon’s theorem, 711 Shift in frequency domain, 36 Shift in time domain, 36, 713 Ship stabilization, 17 Signal-flow graphs, 105, 213 summation point, 94
Index Similarity transformations, 53 Single-branch loop, 106 Single-input–multi-output (SIMO) systems, 81 Single-input–single-output (SISO) systems, 82 Sink node, 105 Sinusoidal function, 30 Sixteen-plant theorem, 662 Slow mode, 162 Source node, 105 Speech control, 21 Speed control system, 126, 252 Speed (or ramp) error constant, 172 Stability, 237 asymptotic, 238, 239, 540 basic theorems, 540 bounded-input, 542 bounded-output, 542 in the circle M, 239 criteria, 243, 543 Jury, 544 Routh, 543 definitions, 237, 540 limit method, 398 of linear time-invariant discrete-time systems, 541 marginal, 239 of practical control systems, 251 relative, 314, 331 in the sense of Lyapunov, 260 of systems described by impulse response matrix, 240 of systems described in state space, 238 of systems described by transfer function matrix, 239 Stabilization of ships, 253 Starting point, 275 State equations, 78, 79, 80, 198 special forms of, 204 State observers, 463 State-space analysis of control systems, 193 State-space design methods, 435, 572 algebraic control, 435 observer, 435, 457 State-space equations, 521 State of a system, 78 State transition matrix, 194, 195, 196 State variables, 193 State vector, 79 controllability, 215
735 observability, 219 [State vector] reconstruction using Luenberger observer, 458 transformations, 204 Steady-state errors, 171, 570 with inputs of special forms, 173 Steady-state response, 149 Strictly equivalent, 92 Structure of a control system, 4 Structured singular value, 657 Submarine, 252 Sun-seeker system, 17 Supersonic airplanes, 285 Sylvester theorems, 57 Synchrosystems, 116 Synthesis problem, 5 System description, 67 System identification, 68, 583 System sensitivity to parameter variations, 167 System time response, 147 Tachometers, 115 Teaching, 22 Temperature control of chamber, 11 Temperature control system, 129 Terminal control problem, 480 Thickness of metal sheets, 289 Time-domain analysis of control systems, 147 Time-invariant system, 518 Time response, graphical representation of, 153 Time scaling, 35 Trace, 48 Tracking problem, 480, 502 Transfer function, 74, 82, 520 Transfer function matrix, 223 Transformed state model, 205 Transient response, 149 method, 396 Transition: from differential equation to transfer function for SISO systems, 87 from an nth-order differential equation to state equations, 211 from one mathematical model to another, 87 from the phase canonical form to the diagonal, 212
736
Index
from state equations to transfer function matrix, 88 [Transition] from transfer function to differential equation for SISO systems, 88 from transfer function matrix to state equations, 88 Trapezoidal method, 532 Tustin transformation method, 532 Type j system, 171 Types of mathematical models, 69 Types of systems, 171
Undamped, 156 Underdamped, 157 Underlying control problem, 621 Uniformly stable, 261 Unit gate function, 28 Unit gate sequence, 708 Unit impulse function, 28 Unit step function, 27 Unit step sequence, 708 Universe, 676
Uncertainty, 638 additive, 640 division, 640 lumped, 639 multiplicative, 639 inverse, 640 neglected dynamics, 639 origins of, 638 parametric, 639 representation of, 639 set, 640 structured, 639 unmodeled dynamics, 639 unstructured, 639
Watt’s centrifugal speed regulator, 3 Weight function, 521 Wheelchair control, 20
Voltage control systems, 122
Yaw of fighter jet, 255 Ziegler-Nichols methods, 396, 570 Z-transform, 707, 711 definition of, 711 inverse, 719 pairs, 724 properties and theorems, 713, 723 of a sum, 714 tables, 723
